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Статті в журналах з теми "080108 Neural, Evolutionary and Fuzzy Computation"
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.
Повний текст джерелаLiu, 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.
Повний текст джерелаCHAN, 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.
Повний текст джерелаSrivastava, 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.
Повний текст джерелаFarook, 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.
Повний текст джерелаPATRINOS, 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.
Повний текст джерелаLi, 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.
Повний текст джерелаWatanabe, 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.
Повний текст джерелаCorns, 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.
Повний текст джерелаLv, 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.
Повний текст джерелаДисертації з теми "080108 Neural, Evolutionary and Fuzzy Computation"
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.
Повний текст джерелаBush, Brian O. "Development of a fuzzy system design strategy using evolutionary computation." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178656308.
Повний текст джерелаAbraham, 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.
Повний текст джерелаYusuf, Syed Adnan. "An evolutionary AI-based decision support system for urban regeneration planning." Thesis, University of Wolverhampton, 2010. http://hdl.handle.net/2436/114896.
Повний текст джерелаFarias, 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.
Повний текст джерелаOne 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
Rosa, Raul Arthur Fernandes 1989. "Redes neurais evolutivas com aprendizado extremo recursivo." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259065.
Повний текст джерелаDissertação (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
(10276349), Anirudh Shankar. "A framework for training Spiking Neural Networks using Evolutionary Algorithms and Deep Reinforcement Learning." Thesis, 2021.
Знайти повний текст джерелаTiwari, A., J. Knowles, E. Avineri, Keshav P. Dahal, and R. Roy. "Applications of Soft Computing." 2006. http://hdl.handle.net/10454/2291.
Повний текст джерела(7551479), Brian Matthew Sutton. "On Spin-inspired Realization of Quantum and Probabilistic Computing." Thesis, 2019.
Знайти повний текст джерела(11073474), Bin Zhang. "Data-driven Uncertainty Analysis in Neural Networks with Applications to Manufacturing Process Monitoring." Thesis, 2021.
Знайти повний текст джерелаArtificial 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.
Книги з теми "080108 Neural, Evolutionary and Fuzzy Computation"
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.
Повний текст джерелаT, 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.
Знайти повний текст джерелаIEEE/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.
Знайти повний текст джерелаInternational 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.
Знайти повний текст джерелаIEEE 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.
Знайти повний текст джерелаIEEE 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.
Знайти повний текст джерелаUniversity, 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.
Знайти повний текст джерелаKeller, James M., David B. Fogel, and Derong Liu. Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation. Wiley & Sons, Incorporated, John, 2016.
Знайти повний текст джерелаKeller, James M., David B. Fogel, and Derong Liu. Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation. Wiley & Sons, Incorporated, John, 2016.
Знайти повний текст джерелаFundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation. Wiley-Interscience, 2016.
Знайти повний текст джерелаЧастини книг з теми "080108 Neural, Evolutionary and Fuzzy Computation"
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.
Повний текст джерелаBuller, 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.
Повний текст джерелаYoshikawa, 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.
Повний текст джерелаLee, 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.
Повний текст джерелаNakanishi, 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.
Повний текст джерелаIshibuchi, 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.
Повний текст джерелаNomura, 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.
Повний текст джерелаAoki, 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.
Повний текст джерелаBranco, 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.
Повний текст джерелаKouzani, 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.
Повний текст джерелаТези доповідей конференцій з теми "080108 Neural, Evolutionary and Fuzzy Computation"
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.
Повний текст джерелаYao, 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.
Повний текст джерелаCiftcioglu, 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.
Повний текст джерелаSharma, 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.
Повний текст джерелаLiang 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.
Повний текст джерелаDuru, 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.
Повний текст джерела"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.
Повний текст джерелаKeyarsalan, 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.
Повний текст джерелаConnolly, 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.
Повний текст джерелаEng-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.
Повний текст джерела