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Salman, Afan Galih. "Pemodelan Sistem Fuzzy Dengan Menggunakan Matlab." ComTech: Computer, Mathematics and Engineering Applications 1, no. 2 (December 1, 2010): 276. http://dx.doi.org/10.21512/comtech.v1i2.2349.
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Fuzzy logic is a method in soft computing category, a method that could process uncertain, inaccurate, and less cost implemented data. Some methods in soft computing category besides fuzzy logic are artificial network nerve, probabilistic reasoning, and evolutionary computing. Fuzzy logic has the ability to develop fuzzy system that is intelligent system in uncertain environment. Some stages in fuzzy system formation process is input and output analysis, determining input and output variable, defining each fuzzy set member function, determining rules based on experience or knowledge of an expert in his field, and implementing fuzzy system. Overall, fuzzy logic uses simple mathematical concept, understandable, detectable uncertain and accurate data. Fuzzy system could create and apply expert experiences directly without exercise process and effort to decode the knowledge into a computer until becoming a modeling system that could be relied on decision making.
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Ekong, Victor. "SOFT COMPUTING SYSTEM FOR THE DIAGNOSIS OF HORMONAL IMBALANCE." Transactions on Machine Learning and Artificial Intelligence 7, no. 6 (January 8, 2020): 30–42. http://dx.doi.org/10.14738/tmlai.76.7507.
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Soft computing, as a science of modelling systems, applies techniques such as evolutionary computing, fuzzy logic, and their hybrids to solve real life problems. Soft computing techniques are quite tolerant to incomplete, imprecise, and uncertainty when dealing with complex situations. This study adopts a hybrid of genetic algorithm and fuzzy logic in diagnosing hormonal imbalance. Hormones are chemical messengers that are vital for growth, reproduction, and are essential for human existence. Hormones may sometimes not be balanced which is a medical condition that often go unnoticed and it’s quite difficult to be diagnosed by medical experts. Hormonal imbalance has several symptoms that could also be confused for other ailments. This proposed system serves as support for medical experts to improve the precision of diagnosis of hormonal imbalance. The study further demonstrates the effective hybridization of genetic algorithm and fuzzy logic in resolving human problems.
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Sadiku, Matthew N. O., Yonghui Wang, Suxia Cui, and Sarhan M. Musa. "SOFT COMPUTING: AN INTRODUCTION." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 6 (June 30, 2018): 63. http://dx.doi.org/10.23956/ijarcsse.v8i6.615.
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Soft computing (SC) is a newly emerging multidisciplinary field. It is a collection of computational techniques, such as expert systems, fuzzy logic, neural networks, and evolutionary algorithms, which provide information processing capabilities to solve complex practical problems. The major benefit of SC lies in its ability to tolerate imprecision, uncertainty, partial truth, and approximation in processing imprecise and inaccurate information and simulating human decision making at low cost. This paper provides a brief introduction on soft computing.
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CHEETHAM, WILLIAM, SIMON SHIU, and ROSINA O. WEBER. "Soft case-based reasoning." Knowledge Engineering Review 20, no. 3 (September 2005): 267–69. http://dx.doi.org/10.1017/s0269888906000579.
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The aim of this commentary is to discuss the contribution of soft computing—a consortium of fuzzy logic, neural network theory, evolutionary computing, and probabilistic reasoning—to the development of case-based reasoning (CBR) systems. We will describe how soft computing has been used in case representation, retrieval, adaptation, reuse, and case-base maintenance, and then present a brief summary of six CBR applications that use soft computing techniques.
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Rahman, Muhammad Muhitur, Md Shafiullah, Syed Masiur Rahman, Abu Nasser Khondaker, Abduljamiu Amao, and Md Hasan Zahir. "Soft Computing Applications in Air Quality Modeling: Past, Present, and Future." Sustainability 12, no. 10 (May 14, 2020): 4045. http://dx.doi.org/10.3390/su12104045.
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Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.
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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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Singh, Sarabjeet, Satvir Singh, and Vijay Kumar Banga. "An Interval Type 2 Fuzzy Logic Framework for Faster Evolutionary Design." Journal of Computational and Theoretical Nanoscience 16, no. 12 (December 1, 2019): 5140–48. http://dx.doi.org/10.1166/jctn.2019.8576.
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In this paper, a fast processing and efficient framework has been proposed to get an optimum output from a noisy data set of a system by using interval type-2 fuzzy logic system. Further, the concept of GPGPU (General Purpose Computing on Graphics Processing Unit) is used for fast execution of the fuzzy rule base on Graphics Processing Unit (GPU). Application of Whale Optimization Algorithm (WOA) is used to ascertain optimum output from noisy data set. Which is further integrated with Interval Type-2 (IT2) fuzzy logic system and executed on Graphics Processing Unit for faster execution. The proposed framework is also designed for parallel execution using GPU and the results are compared with the serial program execution. Further, it is clearly observed that the parallel execution rule base evolved provide better accuracy in less time. The proposed framework (IT2FLS) has been validated with classical bench mark problem of Mackey Glass Time Series. For non-stationary time-series data with additive gaussian noise has been implemented with proposed framework and with T1 FLS. Further, it is observed that IT2 FLS provides better rule base for noisy data set.
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SAFFIOTTI, ALESSANDRO. "Using fuzzy logic for autonomous robotics: an on-line workshop." Knowledge Engineering Review 12, no. 01 (January 1997): 91–94. http://dx.doi.org/10.1017/s0269888997000040.
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In October 1995, Takeshi Furuhashi and his collegues at the Bio-Electronics Laboratory of Nagoya University, Japan, organized the first of a series of on-line workshops, held entirely on the World Wide Web. The advertised advantages of the on-line format were to allow fruitful exchanges while avoiding physical travel, and to guarantee wide visibility of the discussion. The first two workshops in the series were devoted to evolutionary computation; they can be accessed on the web at http://www.bioele.nuee.nagoya-u.ac.jp. The third workshop, named “First On-Line Workshop on Soft Computing” (WSC1), had a broader scope, including all the techniques that go under the heading of “soft computing”, like fuzzy logic, neuro computing, genetic computing, and so on. WSC1 took place from August 19 to 30 1996, and it is accessible on the web at http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/. Because the declared goal of an on-line workshop is to prompt discussion, the rules for submission were looser than in most traditional workshops: papers were not subject to peer review, and it was possible to submit already published papers. All the submitted papers were made visible on the web one week before the workshop, and people could send comments and questions by email during the two workshop weeks; all the questions, comments, and authors' replies are also visible at the WSC1 web site.
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Кричевский, Михаил, and Mihail Krichevskiy. "Selection of the Staff With the Use of Soft Computing." Management of the Personnel and Intellectual Resources in Russia 6, no. 6 (January 23, 2018): 61–65. http://dx.doi.org/10.12737/article_5a4624634bb683.14483599.
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In a changing environment and inaccurate information, it is difficult to get an unambiguous answer about the quality of the candidate for the position, based only on the results of viewing the applicant’s questionnaires. As a consequence, recently there has been a trend towards the use of soft computing (neural networks, fuzzy logic and evolutionary computations) in tasks personnel’s selection. The article presents the solution of such a problem using the methods of soft computing for a software company. We use a neural-fuzzy system such as the ANFIS (Adaptive Network-Based Fuzzy Inference System) to quantify the candidate’s quality. The idea of neural-fuzzy systems is to determine the parameters of fuzzy systems through training methods used in neural networks. The most important advantage of this system lies in the automatic creation of the rules base. After completing the training, we receive an assessment of the quality of the candidate in the form of a scoring on a 10-point scale. In addition, we derive a regression equation that relates the candidate’s quality with the input variables.
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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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Subudhi, Bidyadhar, and Subhakanta Ranasingh. "Evolutionary computing approaches to optimum design of fuzzy logic controller for a flexible robot system." Archives of Control Sciences 23, no. 4 (December 1, 2013): 395–412. http://dx.doi.org/10.2478/acsc-2013-0024.
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Abstract This paper presents the design of a Fuzzy Logic Controller (FLC) whose parameters are optimized by using Genetic Algorithm (GA) and Bacteria Foraging Optimization (BFO) for tip position control of a single link flexible manipulator. The proposed FLC is designed by minimizing the fitness function, which is defined as a function of tip position error, through GA and BFO optimization algorithms achieving perfect tip position tracking of the single link flexible manipulator. Then the tip position responses obtained by using both the above controllers are compared to suggest the best controller for the tip position tracking.
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Perumal, Boominathan, and Aramudhan M. "A Multi-Objective Fuzzy Ant Colony Optimization Algorithm for Virtual Machine Placement." International Journal of Fuzzy System Applications 5, no. 4 (October 2016): 165–91. http://dx.doi.org/10.4018/ijfsa.2016100108.
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In cloud computing, the most important challenge is to enforce proper utilization of physical resources. To accomplish the mentioned challenge, the cloud providers need to take care of optimal mapping of virtual machines to a set of physical machines. In this paper, the authors address the mapping problem as a multi-objective virtual machine placement problem (VMP) and propose to apply multi-objective fuzzy ant colony optimization (F-ACO) technique for optimal placing of virtual machines in the physical servers. VMP-F-ACO is a combination of fuzzy logic and ACO, where we use fuzzy transition probability rule to simulate the behaviour of the ants and the authors apply the same for virtual machine placement problem. The results of fuzzy ACO techniques are compared with five variants of classical ACO, three bin packing heuristics and two evolutionary algorithms. The results show that the fuzzy ACO techniques are better than the other optimization and heuristic techniques considered.
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García-Galán, Sebastián, Rocío Pérez de Prado, and Jose Enrique Munoz Expósito. "Uncertainty-tolerant scheduling strategies for grid computing: knowledge-based techniques with bio-inspired learning." Image Processing & Communications 18, no. 1 (March 1, 2013): 37–44. http://dx.doi.org/10.2478/v10248-012-0073-4.
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Abstract Nowadays, diverse areas in science as high energy physics, astronomy or climate research are increasingly relying on experimental studies addressed with hard computing simulations that cannot be faced with traditional distributed systems. In this context, grid computing has emerged as the new generation computing platform based on the large-scale cooperation of resources. Furthermore, the use of grid computing has also been extended to several technology, engineering or economy areas such as financial services and construction engineering that demand high computer capabilities. Nevertheless, a major issue in the sharing of resources is the scheduling problem in a high-dynamic and uncertain environment where resources may become available, inactive or reserved over time according to local policies or systems failures. In this paper, a review of scheduling strategies dealing with uncertainty in systems information by the application of techniques such as fuzzy logic, neural networks or evolutionary algorithms is presented. Furthermore, this work is centered on the study of scheduling strategies based on fuzzy rulebased systems given their flexibility and ability to adapt to changes in grid systems. These knowledge-based strategies are founded on a fuzzy characterization of the system state and the application of the scheduler knowledge in the form of fuzzy rules to cope with the imprecise environment. Obtaining good rules also arises as a challenging problem. Hence, the main learning methods that allow the improvement and adaptation of the expert schedulers are introduced.
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Lee, E. Stanley. "Soft Computing and Learning Techniques in the Modeling of Humanistic Systems." International Journal of Artificial Life Research 3, no. 4 (October 2012): 1–15. http://dx.doi.org/10.4018/ijalr.2012100101.
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Although modern computer is the most revolutionary and most powerful tool developed in the twenty’s century, it is almost useless for the application of this tool to the not well defined humanistic systems such as politics, law, or even the many hour-to-hour small decisions people make routinely and daily. This is in spite of the fact that the human action of the cognitive band, which is of the order of seconds, is much slower than the speed of the modern computer. In this paper, the author shall first examine the basic differences between the scientific systems and the humanistic systems. Then based on these resulting differences, the author shall propose a neural-soft-computing combined approach, which is naturally suited for the vague and difficult to define humanistic systems. These combined systems are developed during the last approximately twenty years. Yet, it has not applied to the humanistic systems in a systematic and extensive manner. Some of the neural-soft-computing systems, also known as neural-evolutionary operational systems are the combined use of neural network, or support vector machine, with fuzzy system or fuzzy logic and evolutionary operational techniques such as genetic algorithm. Several of these systems are summarized and discussed as to why these systems seem more promising for the handling of humanistic systems. To illustrate the effectiveness of the proposed approaches, several initial applications in the literature are summarized.
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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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Barukčić, Marinko, Srete Nikolovski, and Franjo Jović. "Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation." Journal of Electrical Engineering 61, no. 6 (November 1, 2010): 332–40. http://dx.doi.org/10.2478/v10187-011-0052-1.
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Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation The issue of optimal allocation of capacitor banks concerning power losses minimization in distribution networks are considered in this paper. This optimization problem has been recently tackled by application of contemporary soft computing methods such as: genetic algorithms, neural networks, fuzzy logic, simulated annealing, ant colony methods, and hybrid methods. An evolutionaryheuristic method has been proposed for optimal capacitor allocation in radial distribution networks. An evolutionary method based on genetic algorithm is developed. The proposed method has a reduced number of parameters compared to the usual genetic algorithm. A heuristic stage is used for improving the optimal solution given by the evolutionary stage. A new cost-voltage node index is used in the heuristic stage in order to improve the quality of solution. The efficiency of the proposed two-stage method has been tested on different test networks. The quality of solution has been verified by comparison tests with other methods on the same test networks. The proposed method has given significantly better solutions for time dependent load in the 69-bus network than found in references.
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Mattila, Jorma K. "From Basic Research to Applications." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 5 (September 20, 2005): 497. http://dx.doi.org/10.20965/jaciii.2005.p0497.
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Forty years have passed since Prof. Lotfi A. Zadeh introduced fuzzy set theory in his known article “Fuzzy Sets” in Information and Control, 8, 1965, sparking new development in information technology and automation. This article also formed the roots of the Fuzzy Systems Research Group, an active part of the Laboratory of Applied Mathematics, Lappeenranta University of Technology. Rough set theory, evolutionary computing, and neural computing followed, together with their combinations. This Special Issue presents 10 papers representing these areas. Many of the contributors of this Special Issue belong to the Fuzzy Systems Research Group and others work in close co-operations with this group. The first paper considers the use of linguistically expressed objectives in multicriteria decision-making in selection processes based on topological similarity M-relations between L-sets. The second presents basic ideas and fundamental concepts of rough set theory and considers properties of rough approximations. The third combines Lukasiewicz logics and modifier algebras based on Zadeh algebras, i.e., quasi-Boolean algebras of membership functions. The fourth applies Mö{o}bius transformations, known in complex analysis, to fuzzy subgroups in a topological point of view. The fifth discusses the stability of a classifier based on the Lukasiewicz structure and tests Schweizer and Sklar's implications with an extension to generalized mean to a classification task. The sixth deals with the interpretability problem of first-order Takagi-Sugeno systems and interpolation issues, developing a special two-model configuration. The seventh describes an expert system for defining an athlete's aerobic and anaerobic thresholds that successfully mimics decision-making by sport medicine professionals, with system functionality based on fuzzy comparison measures, generalized means, fuzzy membership functions, and differential evolution. The eighth applies a differential evolution algorithm-based method to training radial basis function networks with variables including centers, weights, and widths. The ninth compares two floating-point-encoded evolutionary algorithms – differential evolution and a generalized generation gap model – using a set of problems with different characteristics. The tenth proposes a new approach for monitoring break tendency of paper webs on modern paper machines, combining linguistic equations and fuzzy logic in a case-based reasoning framework. As the Guest Editor of this Special Issue, I thank the contributors and reviewers for their time and effort in making this special issue possible. I am also grateful to the JACIII editorial board, especially Prof. Kaoru Hirota, the Editors-in-Chief and Managing Editor Kenta Uchino, and the staff of Fuji Technology Press for the opportunity to participate in this work. I also thank Prof. Kaoru Hirota for organizing the reviewing of my paper.
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Statheros, Thomas, Gareth Howells, and Klaus McDonald Maier. "Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques." Journal of Navigation 61, no. 1 (December 10, 2007): 129–42. http://dx.doi.org/10.1017/s037346330700447x.
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This study provides both a spherical understanding about autonomous ship navigation for collision avoidance (CA) and a theoretical background of the reviewed work. Additionally, the human cognitive abilities and the collision avoidance regulations (COLREGs) for ship navigation are examined together with water based collision avoidance algorithms. The requirements for autonomous ship navigation are addressed in conjunction with the factors influencing ship collision avoidance. Humans are able to appreciate these factors and also perform ship navigation at a satisfactory level, but their critical decisions are highly subjective and can lead to error and potentially, to ship collision. The research for autonomous ship navigation may be grouped into the classical and soft computing based categories. Classical techniques are based on mathematical models and algorithms while soft-computing techniques are based on Artificial Intelligence (AI). The areas of AI for autonomous ship collision avoidance are examined in this paper are evolutionary algorithms, fuzzy logic, expert systems, and neural networks (NN), as well as a combination of them (hybrid system).
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K N, Premnath, Srinivasan R, and Elijah Blessing Rajsingh. "Magnetic Field Model (MFM) in Soft Computing and parallelization techniques for Self Organizing Networks (SON) in Telecommunications." International Journal of Energy Optimization and Engineering 3, no. 3 (July 2014): 57–71. http://dx.doi.org/10.4018/ijeoe.2014070104.
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Self Organizing Networks (SON) requires efficient algorithms and effective real time and faster execution techniques to meet the SON requirements (use cases & desired functionalities) (as cited in Srinivasan R and Premnath K N., 2011). The essence of this journal paper is to showcase that Magnetic Field Model (MFM) (as cited in Premnath K N et al., 2013) can be applied in prominent soft computing and parallelization techniques for SON applications, functionalities and use cases. Vast literature and practical approaches are available as part of advancements in Machine Learning, Artificial Intelligence and Fuzzy logic. Based on inspiration from nature's behavior Swarm Intelligence derived from the behaviors of Ant colony and Genetic Algorithms (Evolutionary Algorithms) are some algorithmic techniques to mention.Parallelization of MFM for centralized, hybrid SON use cases is discussed with key inspiration from Google Map Reduce (as cited in Jeffrey Dean and Sanjay Ghemawat., 2004).
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Kashyap, Pankaj Kumar, and Sushil Kumar. "Genetic-fuzzy based load balanced protocol for WSNs." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (April 1, 2019): 1168. http://dx.doi.org/10.11591/ijece.v9i2.pp1168-1183.
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<p><span>Recent advancement in wireless sensor networks primarily depends upon energy constraint. Clustering is the most effective energy-efficient technique to provide robust, fault-tolerant and also enhance network lifetime and coverage. Selection of optimal number of cluster heads and balancing the load of cluster heads are most challenging issues. Evolutionary based approach and soft computing approach are best suitable for counter the above problems rather than mathematical approach. In this paper we propose hybrid technique where Genetic algorithm is used for the selection of optimal number of cluster heads and their fitness value of chromosome to give optimal number of cluster head and minimizing the energy consumption is provided with the help of fuzzy logic approach. Finally cluster heads uses multi-hop routing based on A*(A-star) algorithm to send aggregated data to base station which additionally balance the load. Comparative study among LEACH, CHEF, LEACH-ERE, GAEEP shows that our proposed algorithm outperform in the area of total energy consumption with various rounds and network lifetime, number of node alive versus rounds and packet delivery or packet drop ratio over the rounds, also able to balances the load at cluster head.</span></p>
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Ibric, Svetlana, Zorica Djuric, Jelena Parojcic, and Jelena Petrovic. "Artificial intelligence in pharmaceutical product formulation: Neural computing." Chemical Industry and Chemical Engineering Quarterly 15, no. 4 (2009): 227–36. http://dx.doi.org/10.2298/ciceq0904227i.
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The properties of a formulation are determined not only by the ratios in which the ingredients are combined but also by the processing conditions. Although the relationships between the ingredient levels, processing conditions, and product performance may be known anecdotally, they can rarely be quantified. In the past, formulators tended to use statistical techniques to model their formulations, relying on response surfaces to provide a mechanism for optimization. However, the optimization by such a method can be misleading, especially if the formulation is complex. More recently, advances in mathematics and computer science have led to the development of alternative modeling and data mining techniques which work with a wider range of data sources: neural networks (an attempt to mimic the processing of the human brain); genetic algorithms (an attempt to mimic the evolutionary process by which biological systems self-organize and adapt), and fuzzy logic (an attempt to mimic the ability of the human brain to draw conclusions and generate responses based on incomplete or imprecise information). In this review the current technology will be examined, as well as its application in pharmaceutical formulation and processing. The challenges, benefits and future possibilities of neural computing will be discussed. <br><br> <font color="red"><b>This article has been retracted. Link to the retraction <u><a href="http://dx.doi.org/10.2298/CICEQ1104000E">10.2298/CICEQ1104000E</a><u></b></font>
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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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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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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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Mehta, Ridhima. "Hybrid Fuzzy-Genetic Model for Fitness-Based Performance Optimization in Wireless Networks." International Journal of Computational Intelligence and Applications 20, no. 01 (January 22, 2021): 2150008. http://dx.doi.org/10.1142/s1469026821500085.
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In recent times, the application of autonomic soft computing techniques for design and optimization of wireless access networks is progressively becoming prevalent. These computational learning techniques are capable of handling uncertain and imprecise networking information while driving toward the optimal solution set in the problem search space. The approach proposed by this paper presents the application of the fuzzy logic inference combined with the evolutionary genetic algorithm to optimize the performance parameters in wireless networks. In particular, we consider optimal bit rate allocation and transmission power consumption through the joint design of fuzzy-genetic modeling framework. The sample training data generated through simulations of IEEE 802.11 wireless access network are analyzed for optimization by supplying it to the expert hybrid model comprising of the conjunctive design of both the computational intelligent techniques. Specifically, we contemplate the binary encoding scheme, single-point crossover, reversing mutation, and two fitness functions for executing the binary genetic operations of crossover and mutation. It is generally observed that the proposed hybrid model with polynomial fitness function yields better performance with scalable network datasets than the logarithmic fitness function in terms of higher objective value. Moreover, the results obtained through simulation experiments exhibit significant throughput gains and power efficiency for the deployed fitness functions with the evolving size of training dataset. Compared with the existing methods, our hybrid learning model demonstrates performance enhancement with higher expected fitness measure, improved throughput and power efficiency.
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Juuso, Esko K. "Smart Adaptive Big Data Analysis with Advanced Deep Learning." Open Engineering 8, no. 1 (November 15, 2018): 403–16. http://dx.doi.org/10.1515/eng-2018-0043.
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Abstract Increasing volumes of data, referred as big data, require massive scale and complex computing. Artificial intelligence, deep learning, internet of things and cloud computing are proposed for heterogeneous datasets in hierarchical analytics to manage with the volume, variety, velocity and value of the big data. These solutions are not sufficient in technical systems where measurements, waveform signals, spectral data, images and sparse performance indicators require specific methods for the feature extraction before interactions can be properly analysed. In practical applications, the data analysis, knowledge-based methodologies and optimization need to be combined. The solutions require compact calculation units which can be adaptively modified. The artificial intelligence is extended with various methodologies of computational intelligence. The advanced deep learning approach proposed in this paper uses generalized norms in feature generation, nonlinear scaling in developing compact indicators and linear interactions in model-based systems. The intelligent temporal analysis is available for all indices, including for stress, condition and quality indicators. The service and automation solutions combine these data-driven solutions with the domain expertise by using fuzzy logic for case-based systems. The applications are developed gradually in connections, conversion, cyber, cognition and configuration layers. The advanced methodology is based on the integration of features, scaling functions and interaction models specified by parameters. All the sub-systems and different combinations of them can be recursively updated and optimized with evolutionary computing. The systems adapt to the changing operating conditions and provide situation awareness for the risk analysis. The approach supports different levels of the smart adaptive systems.
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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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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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Habiballa, Hashim, Eva Volna, and Martin Kotyrba. "Automated Generation of EQ-Algebras through Genetic Algorithms." Mathematics 9, no. 8 (April 14, 2021): 861. http://dx.doi.org/10.3390/math9080861.
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This article introduces an approach to the automated generation of special algebras through genetic algorithms. These algorithms can be also used for a broader variety of applications in mathematics. We describe the results of research aiming at automated production of such algebras with the help of evolutionary techniques. Standard approach is not relevant due to the time complexity of the task, which is superexponential. Our research concerning the usage of genetic algorithms enabled the problem to be solvable in reasonable time and we were able to produce finite algebras with special properties called EQ-algebras. EQ-algebras form an alternate truth–value structure for new fuzzy logics. We present the algorithms and special versions of genetic operators suitable for this task. Then we performed experiments with application EQ-Creator are discussed with proper statistical analysis through ANOVA. The genetic approach enables to automatically generate algebras of sufficient extent without superexponential complexity. Our main results include: that elitism is necessary at least for several parent members, a high mutation ratio must be set, optional axioms fulfilment increases computing time significantly, optional properties negatively affect convergence, and colorfulness was defined to prevent trivial solutions (evolution tends to the simplest way of achieving results).
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Pedrycz, W. "Fuzzy evolutionary computing." Soft Computing - A Fusion of Foundations, Methodologies and Applications 2, no. 2 (June 26, 1998): 61–72. http://dx.doi.org/10.1007/s005000050035.
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Saleem, Ayesha, Usman Saleem, Somia Ali, and Misbah Amin. "Need of Computational Intelligence for Post Graduate Students as an Academic Learning." International Journal of Computer Science and Software Engineering 9, no. 2 (February 29, 2020): 6–11. http://dx.doi.org/10.47277/ijcsse/9(2)1.
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The proper definition of intelligence is not widely known and describable within some selected words till now. There is a great controversy on its definition because generally people have not enough knowledge about it. Computational Intelligence is a subset of Artificial Intelligence and based on particular six approaches. These are Fuzzy Logics, Probabilistic Mechanisms, Natural Swarm Intelligence, Neural Networks and Evolutionary Computing. Traditional artificial intelligence use to develop intelligent systems that require proper and comprehensive information about some task to perform. But numerous real-world systems cannot provide exact and complete information about real-world phenomena. On the other hand, the main concern of Computational intelligence is to design intelligent systems that can be able to make decisions on uncertain or ambiguous information and now this becomes basic future system’s need. Both subjects AI and CI have their own importance, but we can analyze that as future needs more intelligent systems, so it required more work, research, understandings and knowledge for computational intelligence. We conduct a survey and meet results that even students of master’s degrees not even know about the term “computational intelligence”. Therefore, this paper proposed that computational intelligence should be an integral subject of courses as enhancement of artificial intelligence related to at least engineering and computer related fields. It will provide knowledge to students and rise their interest for computational intelligence and encourage them to do work to build more intelligent systems that will be able to deal real word problems in future
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Zadeh, L. A. "Fuzzy logic = computing with words." IEEE Transactions on Fuzzy Systems 4, no. 2 (May 1996): 103–11. http://dx.doi.org/10.1109/91.493904.
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Zadeh, L. A. "Soft computing and fuzzy logic." IEEE Software 11, no. 6 (November 1994): 48–56. http://dx.doi.org/10.1109/52.329401.
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Wang, Peizhuang, and Shaohua Tan. "Soft computing and fuzzy logic." Soft Computing - A Fusion of Foundations, Methodologies and Applications 1, no. 1 (April 9, 1997): 35–41. http://dx.doi.org/10.1007/s005000050004.
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dos Santos, M. J., and E. A. de M. Fagotto. "Cloud Computing management using Fuzzy Logic." IEEE Latin America Transactions 13, no. 10 (October 2015): 3392–97. http://dx.doi.org/10.1109/tla.2015.7387246.
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HOMENDA, WLADYSLAW, and WITOLD PEDRYCZ. "BALANCED FUZZY COMPUTING UNIT." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 13, no. 02 (April 2005): 117–38. http://dx.doi.org/10.1142/s0218488505003357.
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We introduce and study a new concept of fuzzy computing units. This construct is is aimed at coping with "negative" (inhibitory) information and accommodating it in the language of fuzzy sets. The essential concept developed in this study deals with computing units exploiting the concept of balanced fuzzy sets. We recall how the membership notion of fuzzy sets can be extended to the [-1,1] range giving rise to balanced fuzzy sets and then summarize properties of augmented (extended) logic operations for these constructs. We show that this idea is particularly appealing in neurocomputing as the "negative" information captured through balanced fuzzy sets exhibits a straightforward correspondence with inhibitory processing mechanisms encountered in neural networks. This gives rise to interesting properties of balanced computing units when compared with fuzzy and logic neurons developed on the basis of classical logic and classical fuzzy sets. Illustrative examples concerning topologies and properties and learning of balanced fuzzy computing units are included. A number of illustrative examples concerning topologies, properties and learning of balanced fuzzy fuzzy computing units are included.
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Zadeh, Lotfi Z. "Fuzzy logic, neural networks and soft computing." Microprocessing and Microprogramming 38, no. 1-5 (September 1993): 13. http://dx.doi.org/10.1016/0165-6074(93)90117-4.
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Zadeh, Lotfi A. "Fuzzy logic, neural networks, and soft computing." Communications of the ACM 37, no. 3 (March 1994): 77–84. http://dx.doi.org/10.1145/175247.175255.
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Dubois, D., and H. Prade. "Soft computing, fuzzy logic, and artificial intelligence." Soft Computing 2, no. 1 (April 1998): 7–11. http://dx.doi.org/10.1007/s005000050025.
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Thakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 128–35. http://dx.doi.org/10.22214/ijraset.2021.37930.
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Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights). Keywords: Artificial Neural Networks (ANNs), Neural Networks (NNs), Fuzzy Logic (FL), Neuro-Fuzzy, Probability Reasoning, Soft Computing, Fuzzification, Defuzzification, Fuzzy Inference Systems, Membership Function.
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Hannachi, M. Skander, Yutaka Hatakeyama, and Kaoru Hirota. "Emulating Qubits with Fuzzy Logic." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 2 (February 20, 2007): 242–49. http://dx.doi.org/10.20965/jaciii.2007.p0242.
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An approach for emulating quantum circuits using conventional analog hardware is presented based on the intuitive similarity between fuzzy logic and quantum superposition, as well as some geometrical analogies. This approach has the advantage of being easy to implement on dedicated hardware for parallel processing of membership functions and fuzzy inference, compared to conventional quantum computing, which requires quantum mechanical systems which are extremely sensitive to noise and difficult to extend to large scale systems. Using geometrical analogies and a suitable transformation, qubits are modeled as pairs of fuzzy membership functions evolving on the unit square and basic one qubit gates are modeled as transformations on this unit square. A fuzzy implementation of the one bit and two bit Deutsch-Jozsa algorithm is proposed. Physical implementation and advantages, such as the possibility of implementing nonlinear or non-unitary gates, as well as drawbacks of the proposed model compared to conventional quantum computing are shown.
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ARAKI, Tomoyuki. "On Complex Vector Fuzzy Logic and Reversible Computing." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 26, no. 1 (2014): 529–37. http://dx.doi.org/10.3156/jsoft.26.529.
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Gupta, M. M. "On fuzzy logic and cognitive computing: Some perspectives." Scientia Iranica 18, no. 3 (June 2011): 590–92. http://dx.doi.org/10.1016/j.scient.2011.04.010.
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Kumar Dutta, Ashit. "Computing with words using intuitionistic fuzzy logic programming." International Journal of Engineering & Technology 7, no. 1.9 (March 1, 2018): 178. http://dx.doi.org/10.14419/ijet.v7i1.9.9815.
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Computing with words is the terminology to indicate a set of numbers and words.It is the base for natural language processing and computational theory of perceptions.It is the art to combine both human and machine perception and find a solution for the real world problems left unsolved due to improper mechanism.Animal voice interpreter, lie detector, driving a vehicle in heavy traffic, and natural language interpreter are the applications need to be automated for the next generation.The computational theory is a group of perceptions used to express propositions in a natural language.The concept of the research is to utilize intutionistic fuzzy logic to interpret perceptions to solve vague problems.The output of the research shows that the performance of proposed method is better than the existing methods.
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Hayat, Bashir, Kyong Hoon Kim, and Ki-Il Kim. "A study on fuzzy logic based cloud computing." Cluster Computing 21, no. 1 (June 3, 2017): 589–603. http://dx.doi.org/10.1007/s10586-017-0953-x.
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Mohammadian, Masoud. "Design of Self-Learning Hierarchical Fuzzy Logic for Guidance and Control of Multirobot Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 6 (December 20, 1999): 446–50. http://dx.doi.org/10.20965/jaciii.1999.p0446.
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Increased application of fuzzy logic to complex control raises a need for a structured methodological approach to developing fuzzy logic systems, which are currently developed based on individualistic bases and cannot face the challenge of interacting with other (fuzzy) systems in a dynamic environment. We propose designing self-learning hierarchical fuzzy logic control based on the integration of evolutionary algorithms and fuzzy logic to provide an integrated knowledge base for intelligent control and collision avoidance among multiple robots. Robots are considered point masses moving in common work space. Evolutionary algorithms are used as an adaptive method for learning the fuzzy knowledge base of control systems and learning, mapping, and interaction between fuzzy knowledge bases of different fuzzy logic systems.
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LOPEZ, VICTORIA, JAVIER MONTERO, LUIS GARMENDIA, and GERMANO RESCONI. "SPECIFICATION AND COMPUTING STATES IN FUZZY ALGORITHMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 03 (June 2008): 301–36. http://dx.doi.org/10.1142/s0218488508005303.
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Since many complex decision making problems can be solved solely by means of an appropriate algorithm, checking the quality of such algorithm is a key issue, even more relevant in the presence of fuzzy uncertainty. In this paper we postulate that the design and formal specification of algorithms can be translated into a fuzzy framework introducing fuzzy first order logic and assert transformations. Following the classical crisp scheme we first formalize the concepts of a fuzzy algorithm specification and a fuzzy computing state, and then a new fuzzy computational logic is presented, so we can derive a computational reasoning for correctness of algorithms. A proposal for the evaluation and setting of suitable degrees of truth to computing states is also introduced.
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Ishibuchi, Hisao. "Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases." Fuzzy Sets and Systems 141, no. 1 (January 2004): 161–62. http://dx.doi.org/10.1016/s0165-0114(03)00262-8.
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Lehal, Manpreet Singh. "Fuzzy in the Real World." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 1 (May 30, 2013): 473–77. http://dx.doi.org/10.24297/ijct.v7i1.3476.
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The Fuzzy Logic tool was introduced in 1965, by LotfiZadeh, and is a mathematical tool for dealing with uncertainty. It offers to a soft computing partnership the important concept of computing with words. It provides a technique to deal with imprecision and information granularity. Fuzzy Logic (FL) is a multi valued logic that allows intermediate values to be defined between conventional evaluations like true/false, yes/no, high/low, etc. Notions like rather tall or very fast can be formulated mathematically and processed by computers, in order to apply a more human like way of thinking in the programming of computers. Fuzzy Logic has emerged as a a profitable tool for the controlling and steering of systems and complex industrial processes, as well as for household and entertainment electronics, as well as for other expert systems and applications like the classification of SAR data.
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Dumitrescu, Catalin, Petrica Ciotirnae, and Constantin Vizitiu. "Fuzzy Logic for Intelligent Control System Using Soft Computing Applications." Sensors 21, no. 8 (April 8, 2021): 2617. http://dx.doi.org/10.3390/s21082617.
Full textAbstract:
When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.