Journal articles on the topic '080108 Neural, Evolutionary and Fuzzy Computation'
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Zhang, Biaobiao, Yue Wu, Jiabin Lu, and K. L. Du. "Evolutionary Computation and Its Applications in Neural and Fuzzy Systems." Applied Computational Intelligence and Soft Computing 2011 (2011): 1–20. http://dx.doi.org/10.1155/2011/938240.
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Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.
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Liu, Ping Ping, and Ying Jun Hao. "Evolutionary Algorithm Research Based on Neural Network for Fuzzy Cognitive Map." Advanced Materials Research 532-533 (June 2012): 1711–15. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1711.
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Fuzzy Cognitive Map (FCM) fails to represent the measures of uncertain causal relationships, proposes an evolutionary algorithm Based on Neural Network for FCM. This algorithm integrates the high non-linear mapping ability of neural network and the globally optimizing ability of evolutionary computation to improve the dynamic reasoning for fuzzy knowledge.
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CHAN, ZEKE S. H., and NIKOLA KASABOV. "EVOLUTIONARY COMPUTATION FOR ON-LINE AND OFF-LINE PARAMETER TUNING OF EVOLVING FUZZY NEURAL NETWORKS." International Journal of Computational Intelligence and Applications 04, no. 03 (September 2004): 309–19. http://dx.doi.org/10.1142/s1469026804001331.
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This work applies Evolutionary Computation to achieve completely self-adapting Evolving Fuzzy Neural Networks (EFuNNs) for operating in both incremental (on-line) and batch (off-line) modes. EFuNNs belong to a class of Evolving Connectionist Systems (ECOS), capable of performing clustering-based, on-line, local area learning and rule extraction. Through Evolutionary Computation, its parameters such as learning rates and membership functions are continuously adjusted to reflect the changes in the dynamics of incoming data. The proposed methods are tested on the Mackey–Glass series and the results demonstrate a substantial improvement in EFuNN's performance.
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Srivastava, Vivek, Bipin K. Tripathi, and Vinay K. Pathak. "Hybrid Computation Model for Intelligent System Design by Synergism of Modified EFC with Neural Network." International Journal of Information Technology & Decision Making 14, no. 01 (January 2015): 17–41. http://dx.doi.org/10.1142/s0219622014500813.
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In recent past, it has been seen in many applications that synergism of computational intelligence techniques outperforms over an individual technique. This paper proposes a new hybrid computation model which is a novel synergism of modified evolutionary fuzzy clustering with associated neural networks. It consists of two modules: fuzzy distribution and neural classifier. In first module, mean patterns are distributed into the number of clusters based on the modified evolutionary fuzzy clustering, which leads the basis for network structure selection and learning in associated neural classifier. In second module, training and subsequent generalization is performed by the associated neural networks. The number of associated networks required in the second module will be same as the number of clusters generated in the first module. Whereas, each network contains as many output neurons as the maximum number of members assigned to each cluster. The proposed hybrid model is evaluated over wide spectrum of benchmark problems and real life biometric recognition problems even in presence of real environmental constraints such as noise and occlusion. The results indicate the efficacy of proposed method over related techniques and endeavor promising outcomes for biometric applications with noise and occlusion.
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Farook, I. Mohammed, S. Dhanalakshmi, V. Manikandan, and C. Venkatesh. "Optimal Feature Selection for Carotid Artery Image Segmentation Using Evolutionary Computation." Applied Mechanics and Materials 626 (August 2014): 79–86. http://dx.doi.org/10.4028/www.scientific.net/amm.626.79.
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Atherosclerosis is hardening of arteries due to high blood pressure and high cholesterol. It causes heart attacks, stroke and peripheral vascular disease and is the major cause of death. In this paper we have attempted a method to identify the presence of plaque in carotid artery from ultrasound images. The ultrasound image is segmented using improved spatial Fuzzy c means algorithm to identify the presence of plaque in carotid artery. Spatial wavelet, Hilbert Huang Transform (HHT), Moment of Gray Level Histogram (MGLH) and Gray Level Co-occurrence Matrix (GLCM) features are extracted from ultrasound images and the feature set is reduced using genetic search process. The intima media thickness is measured using the proposed method. The IMT values are measured from the segmented image and trained using MLBPNN neural network. The neural network classifies the images into normal and abnormal.
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PATRINOS, PANAGIOTIS, ALEX ALEXANDRIDIS, KONSTANTINOS NINOS, and HARALAMBOS SARIMVEIS. "VARIABLE SELECTION IN NONLINEAR MODELING BASED ON RBF NETWORKS AND EVOLUTIONARY COMPUTATION." International Journal of Neural Systems 20, no. 05 (October 2010): 365–79. http://dx.doi.org/10.1142/s0129065710002474.
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In this paper a novel variable selection method based on Radial Basis Function (RBF) neural networks and genetic algorithms is presented. The fuzzy means algorithm is utilized as the training method for the RBF networks, due to its inherent speed, the deterministic approach of selecting the hidden node centers and the fact that it involves only a single tuning parameter. The trade-off between the accuracy and parsimony of the produced model is handled by using Final Prediction Error criterion, based on the RBF training and validation errors, as a fitness function of the proposed genetic algorithm. The tuning parameter required by the fuzzy means algorithm is treated as a free variable by the genetic algorithm. The proposed method was tested in benchmark data sets stemming from the scientific communities of time-series prediction and medicinal chemistry and produced promising results.
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Li, Xiao Guang. "Research on the Development and Applications of Artificial Neural Networks." Applied Mechanics and Materials 556-562 (May 2014): 6011–14. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6011.
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Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.
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Watanabe, Keigo, Kazuhiro Ohkura, and Kiyotaka Izumi. "Selected Papers from SCIS & ISIS 2010 – No.1." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 7 (September 20, 2011): 813. http://dx.doi.org/10.20965/jaciii.2011.p0813.
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SCIS & ISIS is a biennial international joint conference on soft computing and intelligent systems, with research ranging from fuzzy systems, neural networks, and evolutionary computation to multi-agent systems, artificial intelligence, and robotics. SCIS & ISIS 2010 consisted of the 5th International Conference on Soft Computing and Intelligent Systems (SCIS) and the 11th International Symposium on Advanced Intelligent Systems (ISIS), held at Okayama Convention Center on December 8-12, 2010. Original presentations numbered 302 and participants 322. After preliminary selection by SCIS & ISIS 2010 session chairs, we listed over 70 papers to be published in extended form in the Special Issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics. After inviting these authors to submit papers for this special issue, we had two referees to review them and accepted 27 for publication in Vol.15, Nos.7 and 8 in 2011. This special issue presents 15 of these papers covering most conference topics, including fuzzy theory, learning methods, neural networks, and evolutionary computation, with a focus on reinforcement learning, multi-agent system, nonlinear estimation, and real-world applications to visual system, robotics and energy. We thank the authors and reviewers for their invaluable contributions toward making this special issue possible. We are also grateful to Editors-in-chief Prof. Toshio Fukuda of Nagoya University and Prof. Kaoru Hirota of the Tokyo Institute of Technology for inviting us to serve as Guest Editors.
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Corns, Steven Michael. "James Keller, Derong Liu, and David Fogel: Fundamentals of computational intelligence: neural networks, fuzzy systems, and evolutionary computation." Genetic Programming and Evolvable Machines 18, no. 1 (February 2, 2017): 119–20. http://dx.doi.org/10.1007/s10710-017-9285-0.
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Lv, Jian, Miaomiao Zhu, Weijie Pan, and Xiang Liu. "Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns." Information 10, no. 2 (January 22, 2019): 36. http://dx.doi.org/10.3390/info10020036.
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To create alternative complex patterns, a novel design method is introduced in this study based on the error back propagation (BP) neural network user cognitive surrogate model of an interactive genetic algorithm with individual fuzzy interval fitness (IGA-BPFIF). First, the quantitative rules of aesthetic evaluation and the user’s hesitation are used to construct the Gaussian blur tool to form the individual’s fuzzy interval fitness. Then, the user’s cognitive surrogate model based on the BP neural network is constructed, and a new fitness estimation strategy is presented. By measuring the mean squared error, the surrogate model is well managed during the evolution of the population. According to the users’ demands and preferences, the features are extracted for the interactive evolutionary computation. The experiments show that IGA-BPFIF can effectively design innovative patterns matching users’ preferences and can contribute to the heritage of traditional national patterns.
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Xiao, Li Feng, and Hui Tian. "A Review of the Research Progress of Structural Damage Identification Method Based on Computational Intelligence Techniques." Applied Mechanics and Materials 444-445 (October 2013): 1494–502. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.1494.
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This paper presents a comprehensive review of computational Intelligence (CI) technology applied in structural damage identification, clarifies the basic principles of computational intelligence techniques, as well as the applicable difficulties that exist in the field of structural damage identification (SDI) from 6 aspects: fuzzy theory, evidence theory, rough set theory, artificial neural networks, support vector machines and evolutionary computation, and then discussed the applicable prospects of computational Intelligence in SDI. It points out that the reasonable cross-fusion of a variety of CI method to specific research object is a necessary means for SDI research. For economy and practicality considerations, CI is suitable for highly integrated complex structural damage identification.
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PAL, NIKHIL R., and SRIMANTA PAL. "EDITORIAL." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (November 2002): 773–79. http://dx.doi.org/10.1142/s0218001402001976.
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Irrespective of the way computational intelligence (CI) is defined, its components should have the following characteristics: considerable potential in solving real world problems, ability to learn from experience, capability of self-organizing, and ability of adapting in response to dynamically changing conditions and constraints. To summarize, it should display aspects of intelligent behavior as observed in humans. In view of these, we assume that the major ingredients of a computational intelligence system are artificial neural networks, fuzzy sets, rough sets, and evolutionary computation. Some other components that may be parts of computational intelligence (CI) systems are artificial life and immuno computing. It is a synergistic combination of all these components.
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Tanaka, Kazuo. "Advanced Computational Intelligence in Control Theory and Applications." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 2 (April 20, 1999): 67. http://dx.doi.org/10.20965/jaciii.1999.p0067.
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We are witnessing a rapidly growing interest in the field of advanced computational intelligence, a "soft computing" technique. As Prof. Zadeh has stated, soft computing integrates fuzzy logic, neural networks, evolutionary computation, and chaos. Soft computing is the most important technology available for designing intelligent systems and control. The difficulties of fuzzy logic involve acquiring knowledge from experts and finding knowledge for unknown tasks. This is related to design problems in constructing fuzzy rules. Neural networks and genetic algorithms are attracting attention for their potential in raising the efficiency of knowledge finding and acquisition. Combining the technologies of fuzzy logic and neural networks and genetic algorithms, i.e., soft computing techniques will have a tremendous impact on the fields of intelligent systems and control design. To explain the apparent success of soft computing, we must determine the basic capabilities of different soft computing frameworks. Give the great amount of research being done in these fields, this issue addresses fundamental capabilities. This special issue is devoted to advancing computational intelligence in control theory and applications. It contains nine excellent papers dealing with advanced computational intelligence in control theory and applications such as fuzzy control and stability, mobile robot control, neural networks, gymnastic bar action, petroleum plant control, genetic programming, Petri net, and modeling and prediction of complex systems. As editor of this special issue, I believe that the excellent research results it contains provide the basis for leadership in coming research on advanced computational intelligence in control theory and applications.
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Ilin, Vladimir, and Dragan Simić. "A review of computational intelligence methods for traffic management systems." Put i saobraćaj 67, no. 4 (December 17, 2021): 25–30. http://dx.doi.org/10.31075/67.04.05.
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One of the most important challenges in modern city life is to enable effective and efficient traffic management system. Recently, computational intelligence methods have become increasingly popular for traffic management system design, application, and monitoring. Computational intelligence methods are often deployed for managing traffic, that is for reducing mileage, congestion, the use of fuels, and environmental impact. The aim of this paper is twofold. First, to present the three main areas in a computational intelligence approach, namely neural networks, fuzzy logic systems, and evolutionary computation. Second, to emphasize their impact on various traffic management domains, including traffic flow forecasting, traffic light control, traffic fatalities prediction, traffic sign detection, and optimization of transportation networks.
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Ilin, Vladimir, and Dragan Simić. "A review of computational intelligence methods for traffic management systems." Put i saobraćaj 67, no. 4 (December 17, 2021): 25–30. http://dx.doi.org/10.31075/pis.67.04.05.
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One of the most important challenges in modern city life is to enable effective and efficient traffic management system. Recently, computational intelligence methods have become increasingly popular for traffic management system design, application, and monitoring. Computational intelligence methods are often deployed for managing traffic, that is for reducing mileage, congestion, the use of fuels, and environmental impact. The aim of this paper is twofold. First, to present the three main areas in a computational intelligence approach, namely neural networks, fuzzy logic systems, and evolutionary computation. Second, to emphasize their impact on various traffic management domains, including traffic flow forecasting, traffic light control, traffic fatalities prediction, traffic sign detection, and optimization of transportation networks.
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Sivaraman, V., and S. Prakash. "Computational Intelligence in Optimization of Process Parameters in Turning Metals and Composites – A Review." Applied Mechanics and Materials 766-767 (June 2015): 914–20. http://dx.doi.org/10.4028/www.scientific.net/amm.766-767.914.
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In the modern competitive scenario in manufacturing industries, producing products with low cost, less time and good quality are the ultimate goal of any manufacturer. To achieve the goal, several optimization tools are developed to optimize the process parameters of the machining process. Turning is one of the machining processes that cannot be avoided in any manufacturing industries. In this review, optimization of process parameters in turning process by computational intelligence (CI) paradigms for the past ten years is studied. Optimization by CI paradigms such as Fuzzy System (FS), Evolutionary Computation techniques Genetic Algorithm (GA), Swarm Intelligence including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Neural Networks (ANN) etc., is considered. In turning process, surface roughness, tool wear, production time and cost are optimized.
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Lu, Pengzhen, Shengyong Chen, and Yujun Zheng. "Artificial Intelligence in Civil Engineering." Mathematical Problems in Engineering 2012 (2012): 1–22. http://dx.doi.org/10.1155/2012/145974.
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Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.
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VAN ECK, NEES JAN, and LUDO WALTMAN. "BIBLIOMETRIC MAPPING OF THE COMPUTATIONAL INTELLIGENCE FIELD." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 05 (October 2007): 625–45. http://dx.doi.org/10.1142/s0218488507004911.
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In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.
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Guzmán, Juan, Ivette Miramontes, Patricia Melin, and German Prado-Arechiga. "Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification." Axioms 8, no. 1 (January 15, 2019): 8. http://dx.doi.org/10.3390/axioms8010008.
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The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have fuzzy rules and membership functions, which are designed based on the experience of an expert. In this particular case a fuzzy classifier of Mamdani type was built, with 21 rules, with two inputs and one output and the objective of this classifier is to perform blood pressure level classification based on knowledge of an expert which is represented in the fuzzy rules. Subsequently different architectures were made in type-1 and type-2 fuzzy systems for classification, where the parameters of the membership functions used in the design of each architecture were adjusted, which can be triangular, trapezoidal and Gaussian, as well as how the fuzzy rules are optimized based on the ranges established by an expert. The main contribution of this work is the design of the optimized interval type-2 fuzzy system with triangular membership functions. The final type-2 system has a better classification rate of 99.408% than the type-1 classifier developed previously in “Design of an optimized fuzzy classifier for the diagnosis of blood pressure with a new computational method for expert rule optimization” with 98%. In addition, we also obtained a better classification rate than the other architectures proposed in this work.
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Onisawa, Takehisa. "Special Issue on Selected Papers in SCIS & ISIS 2004 - No.1." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 2 (March 20, 2005): 91. http://dx.doi.org/10.20965/jaciii.2005.p0091.
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The Joint Conference of the 2nd International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2004) was held at Keio University in Yokohama, Japan, on September 21-24, 2004. Over 300 papers in various fields, for example, mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks application, evolutionary computation, system modeling, fuzzy measures, and robotics were submitted to the conference. The Program Committee required reviewers in SCIS & ISIS 2004 to select excellent papers considering publication in a special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). Some 45 papers were selected and some of them accepted by other reviewers will be published in a two-part special issue of SCIS & ISIS 2004. In this, the first part, 13 papers have been classified into six groups — papers 1-3 under intelligent control, paper 4 under robotics, papers 5 and 6 under neural network applications, papers 7-9 under evolutionary computation applications, paper 10 under human behavior analysis, and papers 11-13 under image processing. Remaining papers currently under review will be published in the next volume. We thank the reviewers for their time and effort in making these special issues possible so quickly, and thank the JACIII editorial board, especially Profs. Hirota and Fukuda, the Editors-in-Chief and Managing Editor Kenta Uchino for their invaluable aid and advice in putting these special issues together. This issue is dedicated to the late Prof. Toshiro Terano, who passed away on February 15, 2005. He will be greatly missed.
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Kumar, Kanhaiya, and Muskan Kumari. "CONTRIBUTION OF AI TO THE CONSTRUCTION WORLD." BSSS journal of computer 12, no. 1 (June 30, 2021): 58–70. http://dx.doi.org/10.51767/jc1207.
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Artificial intelligence is a department of computer science and information technological know-how, involved in the research, layout, and application of intelligent computer. Conventional techniques for modeling and optimizing complicated structure systems require big amounts of computing assets, and artificial-intelligence-primarily based solutions can frequently provide treasured alternatives for successfully solving problems inside the civil engineering. This paper summarizes currently evolved methods and theories within the growing path for programs of synthetic intelligence in civil engineering, such as evolutionary computation, neural networks, fuzzy systems, professional machine, reasoning, type, and learning, in addition to others like chaos theory, cuckoo seek, firefly algorithm, know-how-based engineering, and simulated annealing. The primary studies tendencies are also talked about in the end. The paper presents an overview of the advances of synthetic intelligence carried out in civil engineering.
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Sarma, Jhumpa. "Role of Artificial Intelligence in Medicine and Clinical Research." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1512–18. http://dx.doi.org/10.22214/ijraset.2021.37617.
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Abstract: Artificial Intelligence is a branch of computer science that enables to analyse complex medical data. The proficiency of artificial intelligence techniques has been explored to a great extent in the field of medicine. Most of the medications go to the business sector after a long tedious process of drug development. It can take a period of 10-15 years or more to convey a medication from its introductory revelation to the hands of the patients. Artificial Intelligence can significantly reduce the time required and can also cut down the expenses by half. Among the methods, artificial neural network is the most widely used analytical tool while other techniques like fuzzy expert systems, natural language processing, robotic process automation and evolutionary computation have been used in different clinical settings. The aim of this paper is to discuss the different artificial intelligence techniques and provide a perspective on the benefits, future opportunities and risks of established artificial intelligence applications in clinical practice on medical education, physicians, healthcare institutions and bioethics. Keywords: Artificial intelligence, clinical trials, medical technologies, artificial neural networks, diagnosis.
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Nakamura, Tsuyoshi. "Selected Papers from SCIS & ISIS 2008 – No.1." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 3 (May 20, 2009): 171. http://dx.doi.org/10.20965/jaciii.2009.p0171.
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Welcome to this special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). I am pleased to introduce 41 selected papers presented at the 3rd International Conference on Soft Computing and Intelligent Systems (SCIS) and the 7th International Symposium on Advanced Intelligent Systems (ISIS) held on September 17-21, 2008, at Nagoya University in Nagoya, Japan. This conference featured 401 original papers in presentations attended by some 500 participants. SCIS & ISIS is a biennial international joint conference in the field of soft computing and intelligent systems, including branches of research ranging from fuzzy systems, neural networks, and evolutionary computation to multiagent systems, artificial intelligence, and robotics. This current issue presents 20 papers covering most of the conference topics including fuzzy theory, self-organizing maps, robotics, computer vision, and optimization algorithms. I would like to thank the authors and reviewers and SCIS & ISIS 2008 for making this special issue possible. I am also grateful to Prof. Toshio Fukuda, Nagoya University, and Prof. Kaoru Hirota, Tokyo Institute of Technology, the editors-in-chief, and the SCIS & ISIS 2008 conference staff for inviting me to guest-edit this Journal.
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Kanoh, Masayoshi. "Special Issue on Selected Papers from SCIS & ISIS 2008 No.2." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 4 (July 20, 2009): 351. http://dx.doi.org/10.20965/jaciii.2009.p0351.
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Welcome to the second special issue on selected papers from SCIS & ISIS 2008, a joint conference combining the 4th Soft Computing and Intelligent Systems (SCIS) and the 9th International Symposium on advances Intelligent Systems (ISIS) held at Nagoya University, Japan, in September 2008. smallskip Three earlier conferences were held in: the National Institute of Advanced Industrial Science and Technology (AIST), Japan (2002); Keio University, Japan (2004); and Tokyo Institute of Technology, Japan (2006). smallskip Conference topics include fuzzy logic, clustering, evolutionary computation, machine learning, rough sets, man-machine interaction/interfaces, neural networks, computer vision, image processing, cognitive modeling, computational intelligence, etc. smallskip Papers of interest containing novel algorithms and ideas in these fields have been selected, so I hope you will enjoy this issue.smallskip I would like to thank the many people who have produced these special issues for SCIS & ISIS2008, and all of the authors and reviewers. Without your help, this issue would not have been possible.
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Kang, Yaohong, Shibin Zhao, and Kazuhiko Kawamoto. "Selected Papers from ISCIIA'04." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 6 (November 20, 2005): 579. http://dx.doi.org/10.20965/jaciii.2005.p0579.
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This special issue contains 14 papers selected from the first International Symposium on Computational Intelligence and Industrial Applications (ISCIIA'04), held in Haikou, China, December 20-24, 2004. Of the 82 papers from 8 countries submitted to the symposium, 62 were accepted for the proceedings. Based on reviewer's recommendations and guest editor's careful consideration, the authors of 14 papers have revised and extended their symposium papers for this issue. Computational intelligence is the study of the design of "intelligent" systems, which is flexible in changing environments and changing goals with uncertainty, and covers artificial intelligence, neural networks, fuzzy systems, evolutionary computation, and hybrid systems. The objective of this special issue is to reveal current challenges, research topics, and technology solutions critical to algorithms and applications involving computational intelligence. These 14 papers cover such important research areas as neural networks, image processing, control, financial engineering, robotics, and related technologies in computational intelligence. We believe that the information in this issue will become a valuable new resource for the computational intelligence community. We thank the authors and referees whose selfless work and valuable comments have made this special issue possible and improved the overall quality of the papers.
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Onisawa, Takehisa. "Special Issue on Selected Papers in SCIS & ISIS 2004 – No.2." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 3 (May 20, 2005): 225. http://dx.doi.org/10.20965/jaciii.2005.p0225.
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The Joint Conference of the 2nd International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2004) held at Keio University in Yokohama, Japan, on September 21-24, 2004, attracted over 300 papers in fields such as mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks applications, evolutionary computation, system modeling, fuzzy measures, and robotics. The Program Committee requested reviewers in SCIS & ISIS 2004 to select papers for a special issue of the Journal of Advanced Computational Intelligence & Intelligent Informatics (JACIII), with 27 papers accepted for publication in a two-part SCIS & ISIS 2004 special – Vol.9, No.2, containing 13 and the second part containing 14. Paper 1 details tap-changer control using neural networks. Papers 2-5 deal with image processing and recognition – Paper 2 proposing a model of saliency-driven scene learning and recognition and applying its model to robotics, paper 3 discussing breast cancer recognition using evolutionary algorithms, paper 4 covering a revised GMDH-typed neural network model applied to medical image recognition, paper 5 presenting how to compensate for missing information in the acquisition of visual information applied to autonomous soccer robot control. Paper 6 details gene expressions networks for 4 fruit fly development stages. Paper 7 proposes an α-constrained particle swarm optimized for solving constrained optimization problem. Paper 8 develops a fuzzy-neuro multilayer perceptron using genetic algorithms for recognizing odor mixtures. Paper 9 discusses how to integrate symbols into neural networks for the fusion of computational and symbolic processing and its effectiveness demonstrated through simulations. Paper 10 proposes an electric dictionary using a set of nodes and links whose usefulness is verified in experiments. Paper 11 presents a multi-agent algorithm for a class scheduling problem, showing its feasibility through computer simulation. Paper 12 proposes inductive temporal formula specification in system verification, reducing memory and time in the task of system verification. Paper 13 applies an agent-based approach to modeling transport using inductive learning by travelers and an evolutionary approach. The last paper analyzes architectural floor plans using a proposed index classifying floor plans from the user's point of view. We thank reviewers for their time and effort in making these special issues available so quickly, and thank the JACIII editorial board, especially Editor-in-Chief Profs. Hirota and Fukuda and Managing Editor Kenta Uchino, for their invaluable aid and advice in putting these special issues together.
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Kubota, Naoyuki. "Selected Papers from SCIS & ISIS 2006 – No.1." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 6 (July 20, 2007): 535. http://dx.doi.org/10.20965/jaciii.2007.p0535.
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SCIS & ISIS is a biennial international joint conference in the field of soft computing and intelligent systems, including branches of researches from fuzzy systems, neural networks, evolutionary computation, multi-agent systems, artificial intelligence or robotics. SCIS & ISIS 2006 falls on the 3rd International Conference on Soft Computing and Intelligent Systems (SCIS) and the 7th International Symposium on Advanced Intelligent Systems (ISIS) held at Tokyo Institute of Technology, in Tokyo, Japan, on September 20-24, 2006. In this conference, 464 original papers were accepted for presentation and the number of attendees was 526. After preliminary selection and review made by the session chairs and the International Program Committees of SCIS & ISIS 2006, we have selected more than 50 papers to be published in extended form in the Special Issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics. The accepted papers are published as the special issues in Vol.11, No.6, 7, and 8 in 2007. This current issue presents 23 papers and covers most of the topics of the conference including fuzzy theories, self-organizing maps, and the optimization of neural networks. The learning and search methods in computational intelligence and real-world applications to image processing, robotics and manufacturing systems are highlighted in this current issue. I would like to thank all the authors and reviewers for their contribution to make this special issue possible. I am also grateful to Prof. Toshio Fukuda, Nagoya University and Prof. Kaoru Hirota, Tokyo Institute of Technology, Editors-in-chief, for inviting me to serve as Guest Editor of this Journal.
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Afrakoti, Iman E. P., and Vahdat Nazerian. "Performance Analysis of Optimization Process on Adaptive Group of Ink Drop Spread Algorithm Proficiency." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 6 (November 4, 2020): 918–24. http://dx.doi.org/10.2174/2352096512666191127122752.
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Aims: Two evolutionary algorithms consist of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are being used for finding the best value of critical parameters in AGIDS which will affect the accuracy and efficiency of the algorithm. Background: Adaptive Group of Ink Drop Spread (AGIDS) is a powerful algorithm which was proposed in fuzzy domain based on Active Learning Method (ALM) algorithm. Objective: The effectiveness of AGIDS vs. artificial neural network and other soft-computing algorithms has been shown in classification, system modeling and regression problems. Methods: For solving a real-world problem a tradeoff should be taken between the needed accuracy and the available time and processing resources. Results: The simulation result shows that optimization approach will affect the accuracy of modelling being better, but its computation time is rather high. Conclusion: The simulation shows that AGIDS algorithm has a suitable efficacy in solving complex problems without using complex optimization algorithms. Other: The simulation shows that AGIDS algorithm has a suitable efficacy in solving complex problems without using complex optimization algorithms.
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Ghoniem, Algarni, and Shaalan. "Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information." Information 10, no. 7 (July 11, 2019): 239. http://dx.doi.org/10.3390/info10070239.
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In multi-modal emotion aware frameworks, it is essential to estimate the emotional features then fuse them to different degrees. This basically follows either a feature-level or decision-level strategy. In all likelihood, while features from several modalities may enhance the classification performance, they might exhibit high dimensionality and make the learning process complex for the most used machine learning algorithms. To overcome issues of feature extraction and multi-modal fusion, hybrid fuzzy-evolutionary computation methodologies are employed to demonstrate ultra-strong capability of learning features and dimensionality reduction. This paper proposes a novel multi-modal emotion aware system by fusing speech with EEG modalities. Firstly, a mixing feature set of speaker-dependent and independent characteristics is estimated from speech signal. Further, EEG is utilized as inner channel complementing speech for more authoritative recognition, by extracting multiple features belonging to time, frequency, and time–frequency. For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. To fuse speech with EEG information, a separate classifier is used for each modality, then output is computed by integrating their posterior probabilities. Results show the superiority of the proposed model, where the overall performance in terms of accuracy average rates is 98.06%, and 97.28%, and 98.53% for EEG, speech, and multi-modal recognition, respectively. The proposed model is also applied to two public databases for speech and EEG, namely: SAVEE and MAHNOB, which achieve accuracies of 98.21% and 98.26%, respectively.
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Iqbal, Sana, Mohammad Sarfraz, Mohammad Ayyub, Mohd Tariq, Ripon K. Chakrabortty, Michael J. Ryan, and Basem Alamri. "A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment." Sustainability 13, no. 13 (June 25, 2021): 7170. http://dx.doi.org/10.3390/su13137170.
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The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.
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Yao, Xin. "Simulated Evolution and Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 2 (March 20, 2000): 129. http://dx.doi.org/10.20965/jaciii.2000.p0129.
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Evolution and learning are two fundamental forms of adaptationl,2). Simulated evolution and learning refers to the study of techniques and methods inspired by Nature for solving complex and difficult real-world problems. These techniques and methods include evolutionary algorithms3), fuzzy learning algorithms, neural learning algorithms, and various statistical learning methods such as nearest neighbor classifiers. In addition to various learning tasks, these techniques and methods have also been applied to various difficult optimization problems that cannot be solved effectively by classical methods (such as mathematical programming methods). This special issue contains six papers selected from those presented at the Second Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'98), Canberra, Australia, 24-27 November 1998. However, all six papers have been rereviewed and substantially extended and revised. They represent significant improved work from their original SEA L'98 papers. The six papers can be grouped into three categories. The first two papers by He et al. and by Ishibuchi and Nakashima described novel applications of genetic algorithms to nearest neighbor classifiers. The next two papers by Kawakami et al. and by Tachibana and Furuhashi presented new fuzzy learning systems. The last two papers by Myung and Kim and by Yu and Wu discussed constrained optimization using the evolutionary approach. I would like to take this opportunity to thank Dr Bob McKay, the SEAL'98 Organizing Committee Chair, for playing a pivotal role in organizing the very successful SEAL'98, Professor Kaoru Hirota, the Editor-in-Chief of the Journal of Advanced Computational Intelligence, for encouraging me to edit this special issue, and all the authors for their high-quality work. References: 1)X. Yao, J-H. Kim, and T. Furuhashi, eds., Simulated Evolution and Learning, Vol. 1285 of Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer-Verlag, 1997. 2)B. Mckay, X. Yao, C. S. Newton, J-H. kim, and T. Furuhashi, eds., Simulated Evolution and Learning, Vo1.1585 of Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer-Verlag, 1999. 3)X. Yao, ed., Evolutionary Computation: Theory and Applications. Singapore: World Scientific Publishing Co., 1999.
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Kasabov, Nikola, and Robert Kozma. "Self-Organization and Adaptation in Intelligent Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 6 (December 20, 1998): 177. http://dx.doi.org/10.20965/jaciii.1998.p0177.
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This special issue is devoted to one of the important topics of current intelligent information systems-their ability to adapt to the environment they operate in, as adaptation is one of the most important features of intelligence. Several milestones in the literature on adaptive systems mark the development in this area. The Hebbian learning rule,1) self-organizing maps,2,3) and adaptive resonance theory4) have influenced the research in this area a great deal. Some current development suggests methods for building adaptive neurofuzzy systems,5) and adaptive self-organizing systems based on principles from biological brains.6) The papers in this issue are organized as follows: The first two papers present material on organization and adaptation in the human brain. The third paper, by Kasabov, presents a novel approach to building open structured adaptive systems for on-line adaptation called evolving connectionist systems. The fourth paper by Kawahara and Saito suggests a method for building virtually connected adaptive cell structures. Papers 5 and 6 discuss the use of genetic algorithms and evolutionary computation for optimizing and adapting the structure of an intelligent system. The last two papers suggest methods for adaptive learning of a sequence of data in a feed-forward neural network that has a fixed structure. References: 1) D.O. Hebb, "The Organization of Behavior," Jwiley, New York, (1949). 2) T. Kohonen, "Self-organisation and associative memory," Springer-Verlag, Berlin, (1988). 3) T. Kohonen, "Self-Organizing Maps, second edition," Springer Verlag, (1997). 4) G. Carpenter and S. Grossberg, "Pattern recognition by self-organizing neural networks," The MIT Press, Cambridge, Massachusetts, (1991). 5) N. Kasabov, "Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering," The MIT Press, CA, MA, (1996). 6) S. Amari and N. Kasabov "Brain-like Computing and Intelligent Information Systems," Springer Verlag, Singapore, (1997).
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Jonathan Lee* and Hsiao-Fan Wang**. "Selected Papers from IFSA'99." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 3 (May 20, 2001): 127. http://dx.doi.org/10.20965/jaciii.2001.p0127.
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The past few years we have witnessed a crystallization of soft computing as a means towards the conception and design of intelligent systems. Soft Computing is a synergetic integration of neural networks, fuzzy logic and evolutionary computation including genetic algorithms, chaotic systems, and belief networks. In this volume, we are featuting seven papers devoted to soft computing as a special issue. These papers are selected from papers submitted to the "The eighth International Fuzzy Systems Association World Congress (IFSA'99)", held in Taipei, Taiwan, in August 1999. Each paper received outstanding recommendations from its reviewers. G-H Tzeng et al. integrate fuzzy numbers, fuzzy regression, and a fuzzy DEA approach as a performance evaluation model for forecasting the productive efficiency of a set of production units when some data are fuzzy numbers. A case of Taipei City Bus Company is adopted for illustration. Y. Shi et al. adopts a fuzzy programming approach to solve a MCMDM (multiple criteria and multiple decision makers) capital budget problem. A solution procedure is proposed to systematically identify a fuzzy optimal selection of possible projects. N. Nguyen et al. propose a new formalism (Chu spaces) to describe parallelism and information flow. Chu spaces provide uniform explanations for different choices of fuzzy methodology, such as choices of fuzzy logical operations of membership functions or defuzzifications. M-C Su et al. propose a technique based on the SOM-based fuzzy systems for voltage security margin estimation. This technique was tested on 1604 simulated data randomly generated from operating conditions on the IEEE 30-bus system to indicate its high efficiency. By defining the concept of approximate dependency and a similarity measure, S-L Wang et al. present a method of using analogical reasoning to infer approximate answers for null queries on similarity-based fuzzy relational databases. K.Yeh et al. use adaptive fuzzy sliding mode control for the structural control of bridges. Combing fuzzy control and sliding mode control can reduce the complexity of fuzzy rule bases and ensure the stability and robustness. This model is demonstrated by three types of bridges, with LRB, sliding isolators and no isolation device. Based on a novel fuzzy clustering algorithm, Y-H Kuo et al. propose an adaptive traffic prediction approach to generalize and unveil the hidden structure of traffic patterns with features of robustness, high accuracy and high adaptability. The periodical, Poisson and real video traffic patterns have been used to verify their approach and investigate its properties. We would like to express our sincere gratitude to everyone who has contributed to this special issue including the authors, the co-reviewers, the JACI Editors-in-Chief Toshio Fukuda and Kaoru Hirota.
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Rudas, Imre. "Complimentary Address." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 1 (January 20, 2017): 6. http://dx.doi.org/10.20965/jaciii.2017.p0006.
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First of all it is my great pleasure to congratulate to the 20th anniversary of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). The first volume of the journal was published in 1997 – and now twenty years later we reached the Vol. 21. Over these years JACIII was one of the forefront and determining journals of soft computing and intelligent informatics and focused to the most up-to-date topic despite the fast vary and evolvement of this research field through its regular and special sessions. One of the greatest merit of the journal is that it is able be serve as a common platform for the most relevant researchers and societies in order to expand the boundaries of many related fields including Fuzzy Logic, Neural Networks, Genetic and Evolutionary Computation, Biologically-Inspired Computation Systems and so on. In these days the most up-coming challenges are connected to the topics of JACIII including the current special issue as well. Self-driving cars, Wearables, Internet-of-Things and many other phenomena are imaginable with the advanced <em>Computational Intelligence</em> behind the rooftop. Expert- and decision-making systems and effective usage of big data request the highly developed <em>Fuzzy Inference Systems</em>. Monitoring of social media in order to get useful information regarding the habits of human users and artificial robots cannot be realized without <em>Web and Artificial Intelligence</em>. Soft-computing based methods and many other intelligent and automated solutions are needed for eligible <em>Data mining</em> – and for the further preparation of the gathered data. Moreover, the demands of future smart city concepts, developed green energy producing solutions and connected power grids present the scientists with specific challenges whereon <em>Smart Grid</em> concept can be the only answer. I would like to invite the Reader to an interesting reading which deals with the aforementioned challenges and scenarios. Furthermore, I hope that JACIII can continue its contribution to the leading-edge research and we can celebrate the 30th anniversary together as well.
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Tambouratzis, Tatiana, John Giannatsis, Andreas Kyriazis, and Panayiotis Siotropos. "Applying the Computational Intelligence Paradigm to Nuclear Power Plant Operation." International Journal of Energy Optimization and Engineering 9, no. 1 (January 2020): 27–109. http://dx.doi.org/10.4018/ijeoe.2020010102.
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In the guise of artificial neural networks (ANNs), genetic/evolutionary computation algorithms (GAs/ECAs), fuzzy logic (FL) inference systems (FLIS) and their variants as well as combinations, the computational intelligence (CI) paradigm has been applied to nuclear energy (NE) since the late 1980s as a set of efficient and accurate, non-parametric, robust-to-noise as well as to-missing-information, non-invasive on-line tools for monitoring, predicting and overall controlling nuclear (power) plant (N(P)P) operation. Since then, the resulting CI-based implementations have afforded increasingly reliable as well as robust performance, demonstrating their potential as either stand-alone tools, or - whenever more advantageous - combined with each other as well as with traditional signal processing techniques. The present review is focused upon the application of CI methodologies to the - generally acknowledged as - key-issues of N(P)P operation, namely: control, diagnostics and fault detection, monitoring, N(P)P operations, proliferation and resistance applications, sensor and component reliability, spectroscopy, fusion supporting operations, as these have been reported in the relevant primary literature for the period 1990-2015. At one end, 1990 constitutes the beginning of the actual implementation of innovative, and – at the same time – robust as well as practical, directly implementable in H/W, CI-based solutions/tools which have proved to be significantly superior to the traditional as well as the artificial-intelligence-(AI)derived methodologies in terms of operation efficiency as well as robustness-to-noise and/or otherwise distorted/missing information. At the other end, 2015 marks a paradigm shift in terms of the emergent (and, swiftly, ubiquitous) use of deep neural networks (DNNs) over existing ANN architectures and FL problem representations, thus dovetailing the increasing requirements of the era of complex - as well as Big - Data and forever changing the means of ANN/neuro-fuzzy construction and application/performance. By exposing the prevalent CI-based tools for each key-issue of N(P)P operation, overall as well as over time for the given 1990-2015 period, the applicability and optimal use of CI tools to NE problems is revealed, thus providing the necessary know-how concerning crucial decisions that need to be made for the increasingly efficient as well as safe exploitation of NE.
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Hirota, Toshio Fukudand Kaoru. "Message from Editors-in-Chief." Journal of Advanced Computational Intelligence and Intelligent Informatics 1, no. 1 (October 20, 1997): 0. http://dx.doi.org/10.20965/jaciii.1997.p0000.
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We are very pleased and honored to have an opportunity to publish a new journal the "International Journal of Advanced Computational Intelligence" (JACI). The JACI is a new, bimonthly journal covering the field of computer science. This journal focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and evolutionary computations, in order to assist in fostering the application of intelligent systems to industry. This new field is called computational intelligence or soft computing. It has already been studied by many researchers, but no single, integrated journal exists anywhere in the world. This new journal gives readers the state of art of the theory and application of Advanced Computational Intelligence. The Topics include, but are not limited to: Fuzzy Logic, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Network Systems, Multimedia, the Human Interface, Biologically-Inspired Evolutionary Systems, Artificial Life, Chaos, Fractal, Wavelet Analysis, Scientific Applications and Industrial Applications. The journal, JACI, is supported by many researchers and scientific organizations, e.g., the International Fuzzy Systems Association (IFSA), the Japan Society of Fuzzy Theory and Systems (SOFT), the Brazilian Society of Automatics (SBA) and The Society of Instrument and Control Engineers (SICE), and we are currently negotiating with the John von Neumann Computer Society (in Hungary). Our policy is to have world-wide communication with many societies and researchers in this field. We would appreciate it if those organizations and people who have an interest in co-sponsorship or have proposals for special issues in this journal, as well as paper submissions, could contact us. Finally our special thanks go to the editorial office of Fuji Technology Press Ltd., especially to its president, Mr. K. Hayashi, and to the editor, Mr. Y. Inoue, for their efforts in publishing this new journal. Lotti A. Zadeh The publication of the International Journal of Advanced Computational Intelligence (JACI) is an important milestone in the advancement of our understanding of how intelligent systems can be conceived, designed, built, and deployed. When one first hears of computational intelligence, a question that naturally arises is: What is the difference, if any, between computational intelligence (CI) and artificial intelligence (AI)? As one who has witnessed the births of both AI and CI, I should like to suggest an answer. As a branch of science and technology, artificial intelligence was born about four decades ago. From the outset, AI was based on classical logic and symbol manipulation. Numerical computations were not welcomed and probabilistic techniques were proscribed. Mainstream AI continued to evolve in this spirit, with symbol manipulation still occupying the center of the stage, but not to the degree that it did in the past. Today, probabilistic techniques and neurocomputing are not unwelcome, but the focus is on distributed intelligence, agents, man-machine interfaces, and networking. With the passage of time, it became increasing clear that symbol manipulation is quite limited in its ability to serve as a foundation for the design of intelligent systems, especially in the realms of robotics, computer vision, motion planning, speech recognition, handwriting recognition, fault diagnosis, planning, and related fields. The inability of mainstream AI to live up to expectations in these application areas has led in the mid-eighties to feelings of disenchantment and widespread questioning of the effectiveness of AI's armamentarium. It was at this point that the name computational intelligence was employed by Professor Nick Cercone of Simon Fraser University in British Columbia to start a new journal named Computational Intelligence -a journal that was, and still is, intended to provide a broader conceptual framework for the conception and design of intelligent systems than was provided by mainstream AI. Another important development took place. The concept of soft computing (SC) was introduced in 1990-91 to describe an association of computing methodologies centering on fuzzy logic (FL), neurocomputing (NC), genetic (or evolutionary) computing (GC), and probabilistic computing (PC). In essence, soft computing differs from traditional hard computing in that it is tolerant of imprecision, uncertainty and partial truth. The basic guiding principle of SC is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality. More recently, the concept of computational intelligence had reemerged with a meaning that is substantially different from that which it had in the past. More specifically, in its new sense, CI, like AI, is concerned with the conception, design, and deployment of intelligent systems. However, unlike mainstream AI, CI methodology is based not on predicate logic and symbol manipulation but on the methodologies of soft computing and, more particularly, on fuzzy logic, neurocomputing, genetic(evolutionary) computing, and probabilistic computing. In this sense, computational intelligence and soft computing are closely linked but not identical. In basic ways, the importance of computational intelligence derives in large measure from the effectiveness of the techniques of fuzzy logic, neurocomputing, genetic (evolutionary) computing, and probabilistic computing in the conception and design of information/intelligent systems, as defined in the statements of the aims and scope of the new journal of Advanced Computational Intelligence. There is one important aspect of both computational intelligence and soft computing that should be stressed. The methodologies which lie at the center of CI and SC, namely, FL, NC, genetic (evolutionary) computing, and PC are for the most part complementary and synergistic, rather than competitive. Thus, in many applications, the effectiveness of FL, NC, GC, and PC can be enhanced by employing them in combination, rather than in isolation. Intelligent systems in which FL, NC, GC, and PC are used in combination are frequently referred to as hybrid intelligent systems. Such systems are likely to become the norm in the not distant future. The ubiquity of hybrid intelligent systems is likely to have a profound impact on the ways in which information/intelligent systems are conceived, designed, built, and interacted with. At this juncture, the most visible hybrid intelligent systems are so-called neurofuzzy systems, which are for the most part fuzzy-rule-based systems in which neural network techniques are employed for system identification, rule induction, and tuning. The concept of neurofuzzy systems was originated by Japanese scientists and engineers in the late eighties, and in recent years has found a wide variety of applications, especially in the realms of industrial control, consumer products, and financial engineering. Today, we are beginning to see a widening of the range of applications of computational intelligence centered on the use of neurofuzzy, fuzzy-genetic, neurogenetic, neurochaotic and neuro-fuzzy-genetic systems. The editors-in-chief of Advanced Computational Intelligence, Professors Fukuda and Hirota, have played and are continuing to play majors roles both nationally and internationally in the development of fuzzy logic, soft computing, and computational intelligence. They deserve our thanks and congratulations for conceiving the International Journal of Advanced Computational Intelligence and making it a reality. International in both spirit and practice, JACI is certain to make a major contribution in the years ahead to the advancement of the science and technology of man-made information/intelligence systems -- systems that are at the center of the information revolution, which is having a profound impact on the ways in which we live, communicate, and interact with the real world. Lotfi A. Zadeh Berkeley, CA, July 24, 1997
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Rudas, Imre J. "Intelligent Engineering Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 4 (July 20, 2000): 237–39. http://dx.doi.org/10.20965/jaciii.2000.p0237.
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The "information revolution" of our time affects our entire generation. While a vision of the "Information Society," with its financial, legal, business, privacy, and other aspects has emerged in the past few years, the "traditional scene" of information technology, that is, industrial automation, maintained its significance as a field of unceasing development. Since the old-fashioned concept of "Hard Automation" applicable only to industrial processes of fixed, repetitive nature and manufacturing large batches of the same product1)was thrust to the background by keen market competition, the key element of this development remained the improvement of "Machine Intelligence". In spite of the fact that L. A. Zadeh already introduced the concept of "Machine Intelligence Quotient" in 1996 to measure machine intelligence2) , this term remained more or less of a mysterious meaning best explicable on the basis of practical needs. The weak point of hard automation is that the system configuration and operations are fixed and cannot be changed without incurring considerable cost and downtime. Mainly it can be used in applications that call for fast and accurate operation in large batch production. Whenever a variety of products must be manufactured in small batches and consequently the work-cells of a production line should be quickly reconfigured to accommodate a change in products, hard automation becomes inefficient and fails due to economic reasons. In these cases, new, more flexible way of automation, so-called "Soft Automation," are expedient and suitable. The most important "ingredient" of soft automation is its adaptive ability for efficiently coping with changing, unexpected or previously unknown conditions, and working with a high degree of uncertainty and imprecision since in practice increasing precision can be very costly. This adaptation must be realized without or within limited human interference: this is one essential component of machine intelligence. Another important factor is that engineering practice often must deal with complex systems of multiple variable and multiple parameter models almost always with strong nonlinear coupling. Conventional analysis-based approaches for describing and predicting the behavior of such systems in many cases are doomed to failure from the outset, even in the phase of the construction of a more or less appropriate mathematical model. These approaches normally are too categorical in the sense that in the name of "modeling accuracy," they try to describe all structural details of the real physical system to be modeled. This significantly increases the intricacy of the model and may result in huge computational burden without considerably improving precision. The best paradigm exemplifying this situation may be the classic perturbation theory: the less significant the achievable correction is, the more work must be invested for obtaining it. Another important component of machine intelligence is a kind of "structural uniformity" giving room and possibility to model arbitrary particular details a priori not specified and unknown. This idea is similar to that of the ready-to-wear industry, whose products can later be slightly modified in contrast to the custom-tailors' made-to-measure creations aiming at maximum accuracy from the beginning. Machines carry out these later corrections automatically. This "learning ability" is another key element of machine intelligence. To realize the above philosophy in a mathematically correct way, L. A. Zadeh separated Hard Computing from Soft Computing. This revelation immediately resulted in distinguishing between two essential complementary branches of machine intelligence: Hard Computing based Artificial Intelligence and Soft Computing based Computational Intelligence. In the last decades, it became generally known that fuzzy logic, artificial neural networks, and probabilistic reasoning based Soft Computing is a fruitful orientation in designing intelligent systems. Moreover, it became generally accepted that soft computing rather than hard computing should be viewed as the foundation of real machine intelligence via exploiting the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality. Further research in the past decade confirmed the view that typical components of present soft computing such as fuzzy logic, neurocomputing, evolutionary computation and probabilistic reasoning are complementary and best results can be obtained by their combined application. These complementary branches of Machine Intelligence, Artificial Intelligence and Computational Intelligence, serve as the basis of Intelligent Engineering Systems. The huge number of scientific results published in journals and conference proceedings worldwide substantiates this statement. Three years ago, a new series of conferences in this direction was initiated and launched with the support of several organizations including the IEEE Industrial Electronics Society and IEEE Hungary Section in technical cooperation with IEEE Robotics & Automation Society. The first event of the series hosted by Bdnki Dondt Polytechnic, Budapest, Hungary, was called "19997 IEEE International Conference on Intelligent Engineering Systems " (INES'97). The Technical University of Vienna, Austria hosted the next event of the series in 1998, followed by INES'99 held by the Technical University of Kosice, Slovakia. The present special issue consists of the extended and revised version of the most interesting papers selected out of the presentations of this conference. The papers exemplify recent development trends of intelligent engineering systems. The first paper pertains to the wider class of neural network applications. It is an interesting report of applying a special Adaptive Resonance Theory network for identifying objects in multispectral images. It is called "Extended Gaussian ARTMAP". The authors conclude that this network is especially advantageous for classification of large, low dimensional data sets. The second paper's subject belongs to the realm of fuzzy systems. It reports successful application of fundamental similarity relations in diagnostic systems. As an example failure detection of rolling-mill transmission is considered. The next paper represents the AI-branch of machine intelligence. The paper is a report on an EU-funded project focusing on the storage of knowledge in a corporate organizational memory used for storing and retrieving knowledge chunks for it. The flexible structure of the system makes it possible to adopt it to different SMEs via using company-specific conceptual terms rather than traditional keywords. The fourth selected paper's contribution is to the field of knowledge discovery. For this purpose in the first step, cluster analysis is done. The method is found to be helpful whenever little or no information on the characteristics of a given data set is available. The next paper approaches scheduling problems by the application of the multiagent system. It is concluded that due to the great number of interactions between components, MAS seems to be well suited for manufacturing scheduling problems. The sixth selected paper's topic is emerging intelligent technologies in computer-aided engineering. It discusses key issues of CAD/CAM technology of our days. The conclusion is that further development of CAD/CAM methods probably will serve companies on the competitive edge. The seventh paper of the selection is a report on seeking a special tradeoff between classical analytical modeling and traditional soft computing. It nonconventionally integrates uniform structures obtained from Lagrangian Classical Mechanics with other simple elements of machine intelligence such as saturated sigmoid transition functions borrowed from neural nets, and fuzzy rules with classical PID/ST, and a simplified version of regression analysis. It is concluded that these different components can successfully cooperate in adaptive robot control. The last paper focuses on the complexity problem of fuzzy and neural network approaches. A fuzzy rule base, be it generated from expert operators or by some learning or identification schemes, may contain redundant, weakly contributing, or outright inconsistent components. Moreover, in pursuit of good approximation, one may be tempted to overly assign the number of antecedent sets, thereby resulting in large fuzzy rule bases and much problems in computation time and storage space. Engineers using neural networks have to face the same complexity problem with the number of neurons and layers. A fuzzy rule base and neural network design, hence, have two important objectives. One is to achieve a good approximation. The other is to reduce the complexity. The main difficulty is that these two objectives are contradictory. A formal approach to extracting the more pertinent elements of a given rule set or neurons is, hence, highly desirable. The last paper is an attempt in this direction. References 1)C. W. De Silva. Automation Intelligence. Engineering Application of Artificial Intelligence. Vol. 7. No. 5. 471-477 (1994). 2)L. A. Zadeh. Fuzzy Logic, Neural Networks and Soft Computing. NATO Advanced Studies Institute on Soft Computing and Its Application. Antalya, Turkey. (1996). 3)L. A. Zadeh. Berkeley Initiative in Soft Computing. IEEE Industrial Electronics Society Newsletter. 41, (3), 8-10 (1994).
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Bonakdari, H., G. Tardif, F. Abram, J. P. Pelletier, and J. Martel-Pelletier. "FRI0416 COMBINATION OF SERUM ADIPOKINES/RELATED INFLAMMATORY FACTORS AND RATIOS AS PREDICTORS OF INFRAPATELLAR FAT PAD VOLUME IN KNEE OSTEOARTHRITIS PATIENTS: USAGE OF A COMPREHENSIVE MACHINE LEARNING APPROACH." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 806.1–807. http://dx.doi.org/10.1136/annrheumdis-2020-eular.1447.
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Background:One of the hurdles in osteoarthritis (OA) drug discovery and the improvement of therapeutic approaches is the early identification of patients who will progress. It is therefore crucial to find efficient and reliable means of screening OA progressors. Although the main risk factors, age, gender and body mass index (BMI), are important, they alone are poor predictors. However, serum factors could be potential biomarkers for early prediction of knee OA progression.Objectives:In a first step toward finding early reliable predictors of OA progressors, this study aimed to determine, in OA individuals, the optimum combination of serum levels of adipokines/related inflammatory factors, their ratios, and the three main OA risk factors for predicting knee OA infrapatellar fat pad (IPFP) volume, as this tissue has been associated with knee OA onset and progression.Methods:Serum and magnetic resonance images (MRI) were from the Osteoarthritis Initiative at baseline. Variables (48) comprised the 3 main OA risk factors (age, gender, BMI), 6 adipokines, 3 inflammatory factors, and their 36 ratios. IPFP volume was assessed on MRI with a neural network methodology. The best variables and models were identified in Total cohort (n=678), High-BMI (n=341) and Low-BMI (n=337), using an artificial intelligence selection approach: the adaptive neuro-fuzzy inference system embedded with fuzzy c-means clustering (ANFIS-FCM). Performance was validated using uncertainty analyses and statistical indices. Reproducibility was done using 80 OA patients from a clinical trial (female, n=57; male, n=23).Results:For the three groups, 8.44E+14 sub-variables were investigated and 48 models were selected. The best model for each group included five variables: the three risk factors and adipsin/C-reactive protein combined for Total cohort, adipsin/chemerin; High-BMI, chemerin/adiponectin high molecular weight; and Low-BMI, interleukin-8. Data also revealed that the main form of the ratio used for the model was justified, as the use of the inverse form slightly decreased the performance of the model in both training and testing stages. Further investigation indicated that gender improved (13-16%) the prediction results compared to the BMI-based models. For each gender, we then generated a pseudocode (an evolutionary computation equation) with the 5 variables for predicting IPFP volume. Reproducibility experiments were excellent (correlation coefficient: female 0.83, male 0.95).Conclusion:This study demonstrates, for the first time, that the combination of the serum levels of adipokines/inflammatory factors and the three main risk factors of OA could predict IPFP volume with high reproducibility, and superior performance with gender separation. By using the models for each gender and the pseudocodes for OA patients provided in this study, the next step will be to develop a predictive model for OA progressors.Acknowledgments:This work was funded by the Chair in Osteoarthritis of the University of Montreal, the Osteoarthritis Research Unit of the University of Montreal Hospital Research Centre, the Groupe de recherches des maladies rhumatismales du Québec and by ArthroLab Inc., all from Montreal, Quebec, Canada.Disclosure of Interests:Hossein Bonakdari: None declared, Ginette Tardif: None declared, François Abram Employee of: ArthroLab Inc., Jean-Pierre Pelletier Shareholder of: ArthroLab Inc., Grant/research support from: TRB Chemedica, Speakers bureau: TRB Chemedica and Mylan, Johanne Martel-Pelletier Shareholder of: ArthroLab Inc., Grant/research support from: TRB Chemedica
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Kawaji, Shigeyasu, and Tetsuo Sawaragi. "Special Issue on Intelligent Control in Coming New Generation." Journal of Robotics and Mechatronics 12, no. 6 (December 20, 2000): 603–4. http://dx.doi.org/10.20965/jrm.2000.p0603.
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In the early 1970s, a concept of intelligent control was proposed by Fu, and since then the advancement of control technologies as a migrate of control theory, artificial intelligence and operations research has been actively attempted. The breakthrough of this concept was to integrate a human judgment and a concept of value as well as management theory into conventional control theoretic approaches, and synthesize these as artificial intelligence. A number of unconventional control techniques have evolved, offering solutions to many difficult control problems in industry and manufacturing. Saridis proposed a general architecture for intelligent control and proposed a design principle of such a hierarchical system as the principle of Increasing Precision with Decreasing Intelligence. During the first generation of intelligent control, a number of intelligent methodologies besides the purely symbolic and logical processing of human knowledge were introduced. They are broadly called soft computing techniques that include artificial neural networks, fuzzy logic, genetic algorithm, and chaos theory. These techniques have contributed much to the advancement of intelligent control from the viewpoint of its ""intelligence"" part, but no solutions are provided from a control theoretic viewpoint, and the definition of intelligence in terms of control theory is still left questionable. To discuss this issue, we initiated a specialist's meeting on survey of intelligent control in 1997 organized under the Institute of Electrical Engineers of Japan, and discussed the current status as well as future perspectives of intelligent control. Some of the papers contributed to this special issue are results obtained in this series of meetings. During that time, the framework of intelligent control has entered the second generation. In the first stage, this framework was discussed in terms of utilized methodologies such as control theory, artificial intelligence, and operations research seeking optimal combinations of these methodologies wherein a distinction is made between the controller, the plant, and the external environment and representations as well as state concepts utilized were a priorily determined and fixed without flexibility. In contrast, the second generation intelligent control system must emphasize a biologically inspired architecture that can accommodate the flexible and dynamic capabilities of living systems including human beings. That is, it must be able to grow and develop increasing capabilities of self-control, self-awareness of representation and reasoning about self and of constructing a coherent whole out of different representations. Actually, a new branch of research on artificial life and system theory of function emergence has shifted the perspectives of intelligence from conventional reductionism to a new design principle based on the concept of ""emergence"". Thus, their approach is quite new in that they attempt to build models that bring together self-organizing mechanisms with evolutionary computation. Such a trend has forced us to reconsider the biological system and/or natural intelligence. In this special issue, we focus on the aspects of semiosis within a multigranular architecture and of emergent properties and techniques for human-machine and/or multiagent collaborative control systems in the coming new generation. These topics are mutually interrelated; the role of multivariable and multiresolutional quantization and clustering for designing intelligent controllers is essential for realizing the abilities to learn unknown multidimensional functions and/or for letting a joint system, which consists of an external environment, a human, and a machine, self-organize distinctive roles in a bottom-up and emerging fashion. This special issue includes papers on proposals of conceptual architecture, methodologies and reports from practical field studies on the hierarchical architecture of machines for realizing hierarchical collaboration and coordination among machine and human autonomies. We believe that these papers will lead to answers to the above questions. We sincerely thank the contributors and reviewers who made this special issue possible. Thanks also go to the editor-in-chief of the Journal of Robotics and Mechatronics, Prof. Makoto Kaneko (Hiroshima University), who provided the opportunity for editing this special issue.
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Ankita Hatiskar. "Data Mining Based Soft Computing Methods For Web Intelligence." International Journal of Advanced Research in Science, Communication and Technology, October 1, 2022, 1–6. http://dx.doi.org/10.48175/ijarsct-7131.
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Data mining is the procedure of extracting interesting knowledge from enormous amounts of data contained in databases, including such patterns, associations, changes, deviations, and prominent structures. Soft Computing Methods such as fuzzy logic, artificial neural network, etc. aims to uncover the potential for error and inaccuracy in order to accomplish scalability, durability, and reduced methods. In today's information age, the Web is the most common distribution medium. Due to its popularity on the Internet, it is widely used in commercial, entertainment, and educational purposes. Web Intelligence (WI) is engaged with the scientific study of the Web's new areas. It is a new area of computer science that integrates artificial intelligence with sophisticated information technology in the framework of the Web, expanding well outside each one of them. In online applications, data mining gives a plethora of possibilities. The biggest concern is figuring out how to identify relevant hidden patterns for improved application. Soft computing techniques such as neural networks, fuzzy logic, support vector machines, and genetic algorithms are used in evolutionary computation to solve this problem. We look at how soft computing approaches may be used to build web intelligences in this research.
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Sahoo, Anik, and Sujoy Baitalik. "Fuzzy Logic, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference Methodology for Soft Computation and Modeling of Ion Sensing Data of a Terpyridyl-Imidazole Based Bifunctional Receptor." Frontiers in Chemistry 10 (March 23, 2022). http://dx.doi.org/10.3389/fchem.2022.864363.
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Anion and cation sensing aspects of a terpyridyl-imidazole based receptor have been utilized in this work for the fabrication of multiply configurable Boolean and fuzzy logic systems. The terpyridine moiety of the receptor is used for cation sensing through coordination, whereas the imidazole motif is utilized for anion sensing via hydrogen bonding interaction and/or anion-induced deprotonation, and the recognition event was monitored through absorption and emission spectroscopy. The receptor functions as a selective sensor for F− and Fe2+ among the studied anions and cations, respectively. Interestingly, the complexation of the receptor by Fe2+ and its decomplexation by F− and deprotonation of the receptor by F− and restoration to its initial form by acid are reversible and can be recycled. The receptor can mimic various logic operations such as combinatorial logic gate and keypad lock using its spectral responses through the sequential use of ionic inputs. Conducting very detailed sensing studies by varying the concentration of the analytes within a wide domain is often very time-consuming, laborious, and expensive. To decrease the time and expenses of the investigations, soft computing approaches such as artificial neural networks (ANNs), fuzzy logic, or adaptive neuro-fuzzy inference system (ANFIS) can be recommended to predict the experimental spectral data. Soft computing approaches to artificial intelligence (AI) include neural networks, fuzzy systems, evolutionary computation, and other tools based on statistical and mathematical optimizations. This study compares fuzzy, ANN, and ANFIS outputs to model the protonation-deprotonation and complexation-decomplexation behaviors of the receptor. Triangular membership functions (trimf) are used to model the ANFIS methodology. A good correlation is observed between experimental and model output data. The testing root mean square error (RMSE) for the ANFIS model is 0.0023 for protonation-deprotonation and 0.0036 for complexation-decomplexation data.
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FURUHASHI, Takeshi. Journal of Japan Society for Fuzzy Theory and Systems 8, no. 2 (1996): 240–42. http://dx.doi.org/10.3156/jfuzzy.8.2_240.
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"Uso de la Inteligencia Artificial para Incrementar la Autonomía de un Robot Móvil Mindstorms NXT en Tareas de Clasificación de Patrones." Revista ECIPeru, December 29, 2018, 50–58. http://dx.doi.org/10.33017/reveciperu2018.0008.
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Uso de la Inteligencia Artificial para Incrementar la Autonomía de un Robot Móvil Mindstorms NXT en Tareas de Clasificación de Patrones Abraham Esteban Gamarra Moreno1, Juan Gamarra Moreno2, Job Daniel Gamarra Moreno3 1 Universidad Nacional del Centro del Perú, Av. Mariscal Castilla N° 3909, Huancayo, Perú 2 Universidad Nacional Mayor de San Marcos, Calle Germán Amézaga N° 375, Lima, Perú 3 Universidad Continental, Av. San Carlos 1980, Huancayo, Perú Recibido el 16 de junio del 2018. Aceptado el 5 de julio del 2018 Resumen La inteligencia artificial es un área que intenta dotar de inteligencia a las máquinas y entre los tópicos que desarrolla están los sistemas expertos, la lógica difusa, los sistemas de planificación, los algoritmos de búsqueda, la computación evolutiva, redes neuronales artificiales entre otros. Los tópicos de la inteligencia artificial que utiliza este artículo son la visión artificial y las redes neuronales artificiales; además utiliza el microbot o robot móvil Mindstorms NXT, que tiene una capacidad limitada en el procesamiento, así como en el almacenamiento de información. La limitación del robot móvil se da porque no tiene a bordo un computador potente para procesar los algoritmos de visión artificial y de las redes neuronales artificiales; por lo que se utiliza un computador externo para realizar su control a través de la tecnología bluetooth. El procesamiento de los algoritmos de visión artificial y de redes neuronales artificiales se realiza en el computador externo y las acciones que ejecuta el robot móvil son enviadas a este, a través de la comunicación bluetooth. El artículo considera que existe autonomía en un robot móvil, cuando este realiza sus acciones sin intervención humana y los indicadores seleccionados para medir esta autonomía son la localización autónoma de los patrones a reconocer y el reconocimiento autónomo o clasificación de estos patrones. La implementación de la localización autónoma de los patrones a reconocer utiliza sensores ópticos, sensores ultrasónicos y el lenguaje de programación C#; así como el reconocimiento autónomo de patrones utiliza una cámara inalámbrica ubicada en el robot móvil, algoritmos de visión artificial, redes neuronales artificiales y el lenguaje de programación visual basic .NET. Los resultados muestran que el promedio del indicador porcentaje de patrones localizados en forma correcta en el entorno por el robot móvil Mindstorms NXT es de 37.81% cuando no se usa la inteligencia artificial y es de 97.18% cuando se usa la inteligencia artificial. Además, el promedio del indicador porcentaje de patrones reconocidos en forma correcta en el entorno por el robot móvil Mindstorms NXT es de 46.25% cuando no se usa la inteligencia artificial y es de 96.87% cuando se usa la inteligencia artificial. Descriptores: inteligencia artificial, visión artificial, redes neuronales, clasificación de patrones, sensores ópticos, sensores de ultrasonido, microbots, Mindstorms NXT. Abstract Artificial intelligence is an area that tries to equip the machines with intelligence and among the topics developed are expert systems, fuzzy logic, planning systems, search algorithms, evolutionary computation, artificial neural networks among others. The topics of artificial intelligence used in this article are artificial vision and artificial neural networks; also uses the microbot or mobile robot Mindstorms NXT, which has a limited capacity in the processing, as well as in the storage of information. The limitation of the mobile robot is because it does not have a powerful computer on board to process artificial vision algorithms and artificial neural networks; so an external computer is used to perform its control through bluetooth technology. The processing of artificial vision algorithms and artificial neural networks is done on the external computer and the actions performed by the mobile robot are sent to it, through bluetooth communication. The article considers that there is autonomy in a mobile robot, when it performs its actions without human intervention and the indicators selected to measure this autonomy are the autonomous localization of the patterns to be recognized and the autonomous recognition or classification of these patterns. The implementation of the autonomous localization of the patterns to be recognized uses optical sensors, ultrasonic sensors and the C # programming language; as well as the autonomous recognition of patterns uses a wireless camera located in the mobile robot, artificial vision algorithms, artificial neural networks and the visual basic .NET programming language. The results show that the average of the indicator percentage of patterns correctly located in the environment by the Mindstorms NXT mobile robot is 37.81% when artificial intelligence is not used and it is 97.18% when artificial intelligence is used. In addition, the average of the indicator percentage of patterns correctly recognized in the environment by the Mindstorms NXT mobile robot is 46.25% when artificial intelligence is not used and is 96.87% when using artificial intelligence. Keywords: artificial intelligence, artificial vision, artificial neural networks, pattern classification, optical sensors, ultrasound sensors, microbots.
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"Uso de la Inteligencia Artificial para Incrementar la Autonomía de un Robot Móvil Mindstorms NXT en Tareas de Clasificación de Patrones." Revista ECIPeru, December 29, 2018, 50–58. http://dx.doi.org/10.33017/reveciperu2018.0008/.
Full textAbstract:
Uso de la Inteligencia Artificial para Incrementar la Autonomía de un Robot Móvil Mindstorms NXT en Tareas de Clasificación de Patrones Abraham Esteban Gamarra Moreno1, Juan Gamarra Moreno2, Job Daniel Gamarra Moreno3 1 Universidad Nacional del Centro del Perú, Av. Mariscal Castilla N° 3909, Huancayo, Perú 2 Universidad Nacional Mayor de San Marcos, Calle Germán Amézaga N° 375, Lima, Perú 3 Universidad Continental, Av. San Carlos 1980, Huancayo, Perú Recibido el 16 de junio del 2018. Aceptado el 5 de julio del 2018 Resumen La inteligencia artificial es un área que intenta dotar de inteligencia a las máquinas y entre los tópicos que desarrolla están los sistemas expertos, la lógica difusa, los sistemas de planificación, los algoritmos de búsqueda, la computación evolutiva, redes neuronales artificiales entre otros. Los tópicos de la inteligencia artificial que utiliza este artículo son la visión artificial y las redes neuronales artificiales; además utiliza el microbot o robot móvil Mindstorms NXT, que tiene una capacidad limitada en el procesamiento, así como en el almacenamiento de información. La limitación del robot móvil se da porque no tiene a bordo un computador potente para procesar los algoritmos de visión artificial y de las redes neuronales artificiales; por lo que se utiliza un computador externo para realizar su control a través de la tecnología bluetooth. El procesamiento de los algoritmos de visión artificial y de redes neuronales artificiales se realiza en el computador externo y las acciones que ejecuta el robot móvil son enviadas a este, a través de la comunicación bluetooth. El artículo considera que existe autonomía en un robot móvil, cuando este realiza sus acciones sin intervención humana y los indicadores seleccionados para medir esta autonomía son la localización autónoma de los patrones a reconocer y el reconocimiento autónomo o clasificación de estos patrones. La implementación de la localización autónoma de los patrones a reconocer utiliza sensores ópticos, sensores ultrasónicos y el lenguaje de programación C#; así como el reconocimiento autónomo de patrones utiliza una cámara inalámbrica ubicada en el robot móvil, algoritmos de visión artificial, redes neuronales artificiales y el lenguaje de programación visual basic .NET. Los resultados muestran que el promedio del indicador porcentaje de patrones localizados en forma correcta en el entorno por el robot móvil Mindstorms NXT es de 37.81% cuando no se usa la inteligencia artificial y es de 97.18% cuando se usa la inteligencia artificial. Además, el promedio del indicador porcentaje de patrones reconocidos en forma correcta en el entorno por el robot móvil Mindstorms NXT es de 46.25% cuando no se usa la inteligencia artificial y es de 96.87% cuando se usa la inteligencia artificial. Descriptores: inteligencia artificial, visión artificial, redes neuronales, clasificación de patrones, sensores ópticos, sensores de ultrasonido, microbots, Mindstorms NXT. Abstract Artificial intelligence is an area that tries to equip the machines with intelligence and among the topics developed are expert systems, fuzzy logic, planning systems, search algorithms, evolutionary computation, artificial neural networks among others. The topics of artificial intelligence used in this article are artificial vision and artificial neural networks; also uses the microbot or mobile robot Mindstorms NXT, which has a limited capacity in the processing, as well as in the storage of information. The limitation of the mobile robot is because it does not have a powerful computer on board to process artificial vision algorithms and artificial neural networks; so an external computer is used to perform its control through bluetooth technology. The processing of artificial vision algorithms and artificial neural networks is done on the external computer and the actions performed by the mobile robot are sent to it, through bluetooth communication. The article considers that there is autonomy in a mobile robot, when it performs its actions without human intervention and the indicators selected to measure this autonomy are the autonomous localization of the patterns to be recognized and the autonomous recognition or classification of these patterns. The implementation of the autonomous localization of the patterns to be recognized uses optical sensors, ultrasonic sensors and the C # programming language; as well as the autonomous recognition of patterns uses a wireless camera located in the mobile robot, artificial vision algorithms, artificial neural networks and the visual basic .NET programming language. The results show that the average of the indicator percentage of patterns correctly located in the environment by the Mindstorms NXT mobile robot is 37.81% when artificial intelligence is not used and it is 97.18% when artificial intelligence is used. In addition, the average of the indicator percentage of patterns correctly recognized in the environment by the Mindstorms NXT mobile robot is 46.25% when artificial intelligence is not used and is 96.87% when using artificial intelligence. Keywords: artificial intelligence, artificial vision, artificial neural networks, pattern classification, optical sensors, ultrasound sensors, microbots.