Relevant bibliographies by topics / 080108 Neural, Evolutionary and Fuzzy Computation

Academic literature on the topic '080108 Neural, Evolutionary and Fuzzy Computation'

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Published: 10 December 2022
Last updated: 28 January 2023

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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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Dissertations / Theses on the topic "080108 Neural, Evolutionary and Fuzzy Computation"

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Creaser, Paul. "Application of evolutionary computation techniques to missile guidance." Thesis, Cranfield University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367124.

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Bush, Brian O. "Development of a fuzzy system design strategy using evolutionary computation." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178656308.

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Abraham, Ajith 1968. "Hybrid soft computing : architecture optimization and applications." Monash University, Gippsland School of Computing and Information Technology, 2002. http://arrow.monash.edu.au/hdl/1959.1/8676.

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Yusuf, Syed Adnan. "An evolutionary AI-based decision support system for urban regeneration planning." Thesis, University of Wolverhampton, 2010. http://hdl.handle.net/2436/114896.

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The renewal of derelict inner-city urban districts suffering from high levels of socio-economic deprivation and sustainability problems is one of the key research areas in urban planning and regeneration. Subject to a wide range of social, economical and environmental factors, decision support for an optimal allocation of residential and service lots within such districts is regarded as a complex task. Pre-assessment of various neighbourhood factors before the commencement of actual location allocation of various public services is considered paramount to the sutainable outcome of regeneration projects. Spatial assessment in such derelict built-up areas requires planning of lot assignment for residential buildings in a way to maximize accessibility to public services while minimizing the deprivation of built neighbourhood areas. However, the prediction of socio-economic deprivation impact on the regeneration districts in order to optimize the location-allocation of public service infrastructure is a complex task. This is generally due to the highly conflicting nature of various service structures with various socio-economic and environmental factors. In regards to the problem given above, this thesis presents the development of an evolutionary AI-based decision support systemto assist planners with the assessment and optimization of regeneration districts. The work develops an Adaptive Network Based Fuzzy Inference System (ANFIS) based module to assess neighbourhood districts for various deprivation factors. Additionally an evolutionary genetic algorithms based solution is implemented to optimize various urban regeneration layouts based upon the prior deprivation assessment model. The two-tiered framework initially assesses socio-cultural deprivation levels of employment, health, crime and transport accessibility in neighbourhood areas and produces a deprivation impact matrix overthe regeneration layout lots based upon a trained, network-based fuzzy inference system. Based upon this impact matrix a genetic algorithm is developed to optimize the placement of various public services (shopping malls, primary schools, GPs and post offices) in a way that maximize the accessibility of all services to regenerated residential units as well as contribute to minimize the measure of deprivation of surrounding neighbourhood areas. The outcome of this research is evaluated over two real-world case studies presenting highly coherent results. The work ultimately produces a smart urban regeneration toolkit which provides designer and planner decision support in the form of a simulation toolkit.
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Farias, Weslley Alves. "Comparação entre controladores fuzzy e neural desenvolvidos via simulação e transferidos para ambientes reais no âmbito da robótica evolutiva." Pós-Graduação em Engenharia Elétrica, 2018. http://ri.ufs.br/jspui/handle/riufs/9569.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
One of the greatest limitations of Evolutionary Robotics is when transfering controllers evolved by simulation to real environments. This limitation is mainly caused by model simplifications and difficulties to represent dynamic characteristics, whether from the robot or the environment. And this results in performance degradation of the evolved controller after the transfer, a phenomenon called reality gap. Because this problem is a limitation for practical and complex applications of evolutionary robotics, many solutions have been proposed since the 90s. Until now, most of the research use control strategies based on artificial neural networks because they allow algorithms to be evolved with less designer influence. On the other hand, fuzzy logic can also be used for the development of controllers in the field of evolutionary robotics because it also allows emulating human intelligence. Therefore, this dissertation investigates whether fuzzy control systems are more robust than neural control systems, both optimized by a genetic algorithm in simulation and later transferred to a real robot in physical environment in the task of autonomous navigation while avoiding obstacles. The results show that in the analyzed conditions, fuzzy controllers present better transfer characteristics, mainly considering the smoothness of the executed trajectory, and an equivalent performance, when compared with neural controllers.
Uma das grandes limitações da Robótica Evolutiva diz respeito à transferência de controladores evoluídos por simulação e transferidos ao ambiente real. Tal limitação devese, sobretudo, a simplificações de modelo e dificuldades na representação de características dinâmicas, tanto do robô quanto do ambiente, e isso resulta na queda de desempenho do controlador evoluído após a transferência, fenômeno denominado de reality gap. Muitas soluções vêm sendo propostas desde a década de 90, em virtude deste problema ser uma limitação para aplicações práticas e complexas da robótica evolutiva. Até o momento, a maioria dos trabalhos de pesquisa desenvolvidos utiliza estratégias de controle baseadas em redes neurais artificiais por permitirem que algoritmos possam ser evoluídos com menor influência do projetista. Por outro lado, a lógica fuzzy também pode ser usada para o desenvolvimento de controladores no âmbito da robótica evolutiva, pois também permite emular a inteligência humana. Portanto, nesta dissertação é investigado se sistemas de controle fuzzy são mais robustos que sistemas de controle neurais, ambos otimizados por um algoritmo genético em simulação e posteriormente transferidos para um robô real em ambiente físico na tarefa de navegação autônoma evitando obstáculos. Como resultado, obteve-se que nas condições analisadas, os controladores fuzzy apresentaram uma melhor transferência, com destaque para a suavidade da trajetória executada, e um desempenho equivalente, quando comparados com controladores neurais.
São Cristóvão, SE
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Rosa, Raul Arthur Fernandes 1989. "Redes neurais evolutivas com aprendizado extremo recursivo." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259065.

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Orientadores: Fernando Antonio Campos Gomide, Marcos Eduardo Ribeiro do Valle Mesquita
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-26T08:06:32Z (GMT). No. of bitstreams: 1 Rosa_RaulArthurFernandes_M.pdf: 8750754 bytes, checksum: 0535142e4de0e75e311aea59a977386e (MD5) Previous issue date: 2014
Resumo: Esta dissertação estuda uma classe de redes neurais evolutivas para modelagem de sistemas a partir de um fluxo de dados. Esta classe é caracterizada por redes evolutivas com estruturas feedforward e uma camada intermediária cujo número de neurônios é variável e determinado durante a modelagem. A aprendizagem consiste em utilizar métodos de agrupamento para estimar o número de neurônios na camada intermediária e algoritmos de aprendizagem extrema para determinar os pesos da camada intermediária e de saída da rede. Neste caso, as redes neurais são chamadas de redes neurais evolutivas. Um caso particular de redes evolutivas é quando o número de neurônios da camada intermediária é determinado a priori, mantido fixo, e somente os pesos da camada intermediária e de saída da rede são atualizados de acordo com dados de entrada. Os algoritmos de agrupamento e de aprendizagem extrema que compõem os métodos evolutivos são recursivos, pois a aprendizagem ocorre de acordo com o processamento de um fluxo de dados. Em particular, duas redes neurais evolutivas são propostas neste trabalho. A primeira é uma rede neural nebulosa híbrida evolutiva. Os neurônios da camada intermediária desta rede são unineurônios, neurônios nebulosos com processamento sináptico realizado por uninormas. Os neurônios da camada de saída são sigmoidais. Um algoritmo recursivo de agrupamento baseado em densidade, chamado de nuvem, é utilizado para particionar o espaço de entrada-saída do sistema e estimar o número de neurônios da camada intermediária da rede; a cada nuvem corresponde um neurônio. Os pesos da rede neural nebulosa híbrida são determinados utilizando a máquina de aprendizado extremo com o algoritmo quadrados mínimos recursivo ponderado. O segundo tipo de rede proposto neste trabalho é uma rede neural multicamada evolutiva com neurônios sigmoidais na camada intermediária e de saída. Similarmente à rede híbrida, nuvens particionam o espaço de entrada-saída do sistema e são utilizadas para estimar o número de neurônios da camada intermediária. O algoritmo para determinar os pesos da rede é a mesma versão recursiva da máquina de aprendizado extremo. Além das redes neurais evolutivas, sugere-se também uma variação da rede adaptativa OS-ELM (online sequential extreme learning machine) mantendo o número de neurônios na camada intermediária fixo e introduzindo neurônios sigmoidais na camada de saída. Neste caso, a aprendizagem usa o algoritmo dos quadrados mínimos recursivo ponderado no aprendizado extremo. As redes foram analisadas utilizando dois benchmarks clássicos: identificação de forno a gás com o conjunto de dados de Box-Jenkins e previsão de série temporal caótica de Mackey-Glass. Dados sintéticos foram gerados para analisar as redes neurais na modelagem de sistemas com parâmetros e estrutura variantes no tempo (concept drif e concept shift). Os desempenhos foram quantificados usando a raiz quadrada do erro quadrado médio e avaliados com o teste estatístico de Deibold-Mariano. Os desempenhos das redes neurais evolutivas e da rede adaptativa foram comparados com os desempenhos da rede neural com aprendizagem extrema e dos métodos de modelagem evolutivos representativos do estado da arte. Os resultados mostram que as redes neurais evolutivas sugeridas neste trabalho são competitivas e têm desempenhos similares ou superiores às abordagens evolutivas propostas na literatura
Abstract: Abstract: This dissertation studies a class of evolving neural networks for system modeling from data streams. The class encompasses single hidden layer feedforward neural networks with variable and online de nition of the number of hidden neurons. Evolving neural network learning uses clustering methods to estimate the number of hidden neurons simultaneously with extreme learning algorithms to compute the weights of the hidden and output layers. A particular case is when the evolving network keeps the number of hidden neurons xed. In this case, the number of hidden neurons is found a priori, and the hidden and output layer weights updated as data are input. Clustering and extreme learning algorithms are recursive. Therefore, the learning process may occur online or real-time using data stream as input. Two evolving neural networks are suggested in this dissertation. The rst is na evolving hybrid fuzzy neural network with unineurons in the hidden layer. Unineurons are fuzzy neurons whose synaptic processing is performed using uninorms. The output neurons are sigmoidals. A recursive clustering algorithm based on density and data clouds is used to granulate the input-output space, and to estimate the number of hidden neurons of the network. Each cloud corresponds to a hidden neuron. The weights of the hybrid fuzzy neural network are found using the extreme learning machine and the weighted recursive least squares algorithm. The second network is an evolving multilayer neural network with sigmoidal hidden and output neurons. Like the hybrid neural fuzzy network, clouds granulate the input-output space and gives the number of hidden neurons. The algorithm to compute the network weights is the same recursive version of the extreme learning machine. A variation of the adaptive OS-ELM (online sequential extreme learning machine) network is also suggested. Similarly as the original, the new OS-ELM xes the number of hidden neurons, but uses sigmoidal instead of linear neurons in the output layer. The new OS-ELM also uses weighted recursive least square.The hybrid and neural networks were evaluated using two classic benchmarks: the gas furnace identi cation using the Box-Jenkins data, and forecasting of the chaotic Mackey-Glass time series. Synthetic data were produced to evaluate the neural networks when modeling systems with concept drift and concept shift. This a modeling circumstance in which system structure and parameters change simultaneously. Evaluation was done using the root mean square error and the Deibold-Mariano statistical test. The performance of the evolving and adaptive neural networks was compared against neural network with extreme learning, and evolving modeling methods representative of the current state of the art. The results show that the evolving neural networks and the adaptive network suggested in this dissertation are competitive and have similar or superior performance than the evolving approaches proposed in the literature
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica
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(10276349), Anirudh Shankar. "A framework for training Spiking Neural Networks using Evolutionary Algorithms and Deep Reinforcement Learning." Thesis, 2021.

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In this work two novel frameworks, one using evolutionary algorithms and another using Reinforcement Learning for training Spiking Neural Networks are proposed and analyzed. A novel multi-agent evolutionary robotics (ER) based framework, inspired by competitive evolutionary environments in nature, is demonstrated for training Spiking Neural Networks (SNN). The weights of a population of SNNs along with morphological parameters of bots they control in the ER environment are treated as phenotypes. Rules of the framework select certain bots and their SNNs for reproduction and others for elimination based on their efficacy in capturing food in a competitive environment. While the bots and their SNNs are given no explicit reward to survive or reproduce via any loss function, these drives emerge implicitly as they evolve to hunt food and survive within these rules. Their efficiency in capturing food as a function of generations exhibit the evolutionary signature of punctuated equilibria. Two evolutionary inheritance algorithms on the phenotypes, Mutation and Crossover with Mutation along with their variants, are demonstrated. Performances of these algorithms are compared using ensembles of 100 experiments for each algorithm. We find that one of the Crossover with Mutation variants promotes 40% faster learning in the SNN than mere Mutation with a statistically significant margin. Along with an evolutionary approach to training SNNs, we also describe a novel Reinforcement Learning(RL) based framework using the Proximal Policy Optimization to train a SNN for an image classification task. The experiments and results of the framework are then discussed highlighting future direction of the work.
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Tiwari, A., J. Knowles, E. Avineri, Keshav P. Dahal, and R. Roy. "Applications of Soft Computing." 2006. http://hdl.handle.net/10454/2291.

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(7551479), Brian Matthew Sutton. "On Spin-inspired Realization of Quantum and Probabilistic Computing." Thesis, 2019.

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The decline of Moore's law has catalyzed a significant effort to identify beyond-CMOS devices and architectures for the coming decades. A multitude of classical and quantum systems have been proposed to address this challenge, and spintronics has emerged as a promising approach for these post-Moore systems. Many of these architectures are tailored specifically for applications in combinatorial optimization and machine learning. Here we propose the use of spintronics for such applications by exploring two distinct but related computing paradigms. First, the use of spin-currents to manipulate and control quantum information is investigated with demonstrated high-fidelity gate operation. This control is accomplished through repeated entanglement and measurement of a stationary qubit with a flying-spin through spin-torque like effects. Secondly, by transitioning from single-spin quantum bits to larger spin ensembles, we then explore the use of stochastic nanomagnets to realize a probabilistic system that is intrinsically governed by Boltzmann statistics. The nanomagnets explore the search space at rapid speeds and can be used in a wide-range of applications including optimization and quantum emulation by encoding the solution to a given problem as the ground state of the equivalent Boltzmann machine. These applications are demonstrated through hardware emulation using an all-digital autonomous probabilistic circuit.
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(11073474), Bin Zhang. "Data-driven Uncertainty Analysis in Neural Networks with Applications to Manufacturing Process Monitoring." Thesis, 2021.

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Artificial neural networks, including deep neural networks, play a central role in data-driven science due to their superior learning capacity and adaptability to different tasks and data structures. However, although quantitative uncertainty analysis is essential for training and deploying reliable data-driven models, the uncertainties in neural networks are often overlooked or underestimated in many studies, mainly due to the lack of a high-fidelity and computationally efficient uncertainty quantification approach. In this work, a novel uncertainty analysis scheme is developed. The Gaussian mixture model is used to characterize the probability distributions of uncertainties in arbitrary forms, which yields higher fidelity than the presumed distribution forms, like Gaussian, when the underlying uncertainty is multimodal, and is more compact and efficient than large-scale Monte Carlo sampling. The fidelity of the Gaussian mixture is refined through adaptive scheduling of the width of each Gaussian component based on the active assessment of the factors that could deteriorate the uncertainty representation quality, such as the nonlinearity of activation functions in the neural network.

Following this idea, an adaptive Gaussian mixture scheme of nonlinear uncertainty propagation is proposed to effectively propagate the probability distributions of uncertainties through layers in deep neural networks or through time in recurrent neural networks. An adaptive Gaussian mixture filter (AGMF) is then designed based on this uncertainty propagation scheme. By approximating the dynamics of a highly nonlinear system with a feedforward neural network, the adaptive Gaussian mixture refinement is applied at both the state prediction and Bayesian update steps to closely track the distribution of unmeasurable states. As a result, this new AGMF exhibits state-of-the-art accuracy with a reasonable computational cost on highly nonlinear state estimation problems subject to high magnitudes of uncertainties. Next, a probabilistic neural network with Gaussian-mixture-distributed parameters (GM-PNN) is developed. The adaptive Gaussian mixture scheme is extended to refine intermediate layer states and ensure the fidelity of both linear and nonlinear transformations within the network so that the predictive distribution of output target can be inferred directly without sampling or approximation of integration. The derivatives of the loss function with respect to all the probabilistic parameters in this network are derived explicitly, and therefore, the GM-PNN can be easily trained with any backpropagation method to address practical data-driven problems subject to uncertainties.

The GM-PNN is applied to two data-driven condition monitoring schemes of manufacturing processes. For tool wear monitoring in the turning process, a systematic feature normalization and selection scheme is proposed for the engineering of optimal feature sets extracted from sensor signals. The predictive tool wear models are established using two methods, one is a type-2 fuzzy network for interval-type uncertainty quantification and the other is the GM-PNN for probabilistic uncertainty quantification. For porosity monitoring in laser additive manufacturing processes, convolutional neural network (CNN) is used to directly learn patterns from melt-pool patterns to predict porosity. The classical CNN models without consideration of uncertainty are compared with the CNN models in which GM-PNN is embedded as an uncertainty quantification module. For both monitoring schemes, experimental results show that the GM-PNN not only achieves higher prediction accuracies of process conditions than the classical models but also provides more effective uncertainty quantification to facilitate the process-level decision-making in the manufacturing environment.

Based on the developed uncertainty analysis methods and their proven successes in practical applications, some directions for future studies are suggested. Closed-loop control systems may be synthesized by combining the AGMF with data-driven controller design. The AGMF can also be extended from a state estimator to the parameter estimation problems in data-driven models. In addition, the GM-PNN scheme may be expanded to directly build more complicated models like convolutional or recurrent neural networks.

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Books on the topic "080108 Neural, Evolutionary and Fuzzy Computation"

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Furuhashi, Takeshi, and Yoshiki Uchikawa, eds. Fuzzy Logic, Neural Networks, and Evolutionary Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7.

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T, Furuhashi, and Uchikawa Y. 1941-, eds. Fuzzy logic, neural networks, and evolutionary computation: IEEE/Nagoya University World Wisepersons Workshop, Nagoya, Japan, November 14-15, 1995 : selected papers. Berlin: Springer, 1996.

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IEEE/Nagoya-University World Wisepersons Workshop on Fuzzy Logic and Neural Networks/Evolutionary Computation (1995 Nagoya, Japan). Proceedings of the 1995 IEEE/Nagoya-University World Wisepersons Workshop (WWW '95) on Fuzzy Logic and Neural Networks/Evolutionary Computation: November 14 and 15, 1995, Rubrum Ohzan, Nagoya, Japan. Nagoya, Japan: Nagoya University, 1995.

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International Conference on Microelectronics for Neural, Fuzzy and Bio-inspired Systems (7th 1999 Granada, Spain). MicroNeuro'99: Proceedings of the Seventh International Conference on Microelectronics for Neural, Fuzzy and Bio-inspired Systems : April 7-9, 1999, Granada, Spain. Los Alamitos, California: IEEE Computer Society Press, 1999.

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IEEE Conference on Soft Computing on Industrial Applications (2008 Muroran-shi, Japan). 2008 IEEE Conference on Soft Computing on Industrial Applications (SMCia/08) : Muroran, Japan, 25-27 June 2008. [Piscataway, N.J.]: IEEE, 2008.

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IEEE Mountain Workshop on Soft Computing in Industrial Applications (2001 Blacksburg, Virginia). SMCia/01: Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications : Donaldson-Brown Hotel, Virginia Tech, Blacksburg, Virginia, U.S.A., June 25-27, 2001. Piscataway, New Jersey: IEEE, 2001.

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University, Brigham Young, IEEE Instrumentation and Measurement Society. TC-22--Intelligent Measurement Systems., and IEEE Instrumentation and Measurement Society., eds. SCIMA 2003: 2003 IEEE International Workshop on Soft Computing in Instrumentation, Measuremment and Related Applications : Brigham Young University, Provo, Utah, USA, 17 May, 2003. Piscataway, N.J: IEEE, 2003.

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Keller, James M., David B. Fogel, and Derong Liu. Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation. Wiley & Sons, Incorporated, John, 2016.

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Keller, James M., David B. Fogel, and Derong Liu. Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation. Wiley & Sons, Incorporated, John, 2016.

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Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation. Wiley-Interscience, 2016.

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Book chapters on the topic "080108 Neural, Evolutionary and Fuzzy Computation"

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Linkens, Derek A., and H. Okola Nyongesa. "Evolutionary Learning in Neural Fuzzy Control Systems." In Fuzzy Evolutionary Computation, 199–222. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_9.

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Buller, Andrzej. "Fizzy-Fuzzy inferencing." In Fuzzy Logic, Neural Networks, and Evolutionary Computation, 172–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7_21.

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Yoshikawa, Tomohiro, Takeshi Furuhashi, and Yoshiki Uchikawa. "Acquisition of fuzzy rules from DNA coding method." In Fuzzy Logic, Neural Networks, and Evolutionary Computation, 73–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7_17.

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Lee, Michael A., Henrik Esbensen, and Laurent Lemaitre. "The design of hybrid fuzzy/evolutionary multiobjective optimization algorithms." In Fuzzy Logic, Neural Networks, and Evolutionary Computation, 1–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7_13.

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Nakanishi, Shohachiro, Akihiro Ohtake, Ronald R. Yager, Shinobu Ohtani, and Hiroaki Kikuchi. "Structure identification of acquired knowledge in fuzzy inference by genetic algorithms." In Fuzzy Logic, Neural Networks, and Evolutionary Computation, 21–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7_14.

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Ishibuchi, Hisao, Tomoharu Nakashima, and Tadahiko Murata. "A fuzzy classifier system that generates linguistic rules for pattern classification problems." In Fuzzy Logic, Neural Networks, and Evolutionary Computation, 35–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7_15.

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Nomura, Tatsuya, and Tsutomu Miyoshi. "Numerical coding and unfair average crossover in GA for fuzzy rule extraction in dynamic environments." In Fuzzy Logic, Neural Networks, and Evolutionary Computation, 55–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7_16.

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Aoki, Takeshi, Toshiaki Oka, Soichiro Hayakawa, Tatsuya Suzuki, and Shigeru Okuma. "Experimental study on acquisition of optimal action for autonomous mobile robot to avoid moving multiobstacles." In Fuzzy Logic, Neural Networks, and Evolutionary Computation, 89–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7_18.

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Branco, P. J. Costa, N. Lori, and J. A. Dente. "New approaches on structure identification of fuzzy models: Case study in an electro-mechanical system." In Fuzzy Logic, Neural Networks, and Evolutionary Computation, 104–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7_19.

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Kouzani, Abbas Z., and Abdesselam Bouzerdoum. "A generic fuzzy neuron and its application to motion estimation." In Fuzzy Logic, Neural Networks, and Evolutionary Computation, 144–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7_20.

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Conference papers on the topic "080108 Neural, Evolutionary and Fuzzy Computation"

1

Cai, Alvin, Chai Quek, and Douglas L. Maskell. "Type-2 GA-TSK fuzzy neural network." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424661.

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Yao, Shuo, Michel Pasquier, and Chai Quek. "A foreign exchange portfolio management mechanism based on fuzzy neural networks." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424795.

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Ciftcioglu, Ozer, and Michael S. Bittermann. "Fuzzy neural tree in evolutionary computation for architectural design cognition." In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7257171.

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Sharma, S. K., G. W. Irwin, and R. Sutton. "Fuzzy logic for priority based genetic search in evolving a neural network architecture." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424671.

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Liang Zhao and Fei-Yue Wang. "Design for self-organizing fuzzy neural networks using a novel hybrid learning algorithm." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424850.

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Duru, Okan, and Matthew Butler. "Stationarity control in the fuzzy time series and neural network algorithms." In 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016. http://dx.doi.org/10.1109/cec.2016.7744228.

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"OPTIMIZATION OF STRUCTURE OF FUZZY-NEURAL SYSTEMS USING COEVOLUTIONARY ALGORITHM." In International Conference on Evolutionary Computation Theory and Applications. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003642001250130.

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Keyarsalan, M., GH A. Montazer, and K. Kazemi. "Font-based persian character recognition using Simplified Fuzzy ARTMAP neural network improved by fuzzy sets and Particle Swarm Optimization." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983322.

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Connolly, Jean-Francois, Eric Granger, and Robert Sabourin. "An adaptive ensemble of fuzzy ARTMAP neural networks for video-based face classification." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5585941.

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Eng-Yeow Cheu, See-Kiong Ng, and Hiok-Chai Quek. "An Interval type-2 Neural Fuzzy Inference System based on Piaget's action-cognitive paradigm." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983044.

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