University of Bahrain
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Evolving Fuzzy Neural Networks by Particle Swarm Optimization with Fuzzy Genotype Values

Show simple item record Okada, Hidehiko 2018-07-09T05:42:55Z 2018-07-09T05:42:55Z 2014
dc.identifier.issn 2210-142X
dc.description.abstract Particle swarm optimization (PSO) is a well-known instance of swarm intelligence algorithms and there have been many researches on PSO. In this paper, the author proposes an extension of PSO for solving fuzzy-valued optimization problems. In the proposed extension, genotype values (i.e. values in particle position vectors) are not real numbers but fuzzy numbers. Search processes in PSO are extended so that PSO can handle genotype instances with fuzzy numbers. The proposed method is experimentally applied to evolution of neural networks with fuzzy weights and biases. Experimental results showed that fuzzy neural networks evolved by the proposed method could model hidden target fuzzy functions despite the fact that no training data was explicitly provided. en_US
dc.language.iso en_US en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International *
dc.rights.uri *
dc.subject Evolutionary algorithm en_US
dc.subject Swarm intelligence en_US
dc.subject Particle swarm optimization en_US
dc.subject Fuzzy number en_US
dc.subject Feedforward neural network en_US
dc.subject Neuroevolution en_US
dc.title Evolving Fuzzy Neural Networks by Particle Swarm Optimization with Fuzzy Genotype Values en_US
dc.type Article en_US
dc.volume 03
dc.issue 03
dc.source.title International Journal of Computing and Digital Systems
dc.abbreviatedsourcetitle IJCDS

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