Training method for a feed forward neural network based on meta-heuristics
This paper proposes a Gaussian-Cauchy Particle Swarm Optimization (PSO) algorithm to provide the optimized parameters for a Feed Forward Neural Network. The improved PSO trains the Neural Network by optimizing the network weights and bias in the Neural Network. In comparison with the Back Propagatio...
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| Main Authors: | , , , |
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| Format: | Article |
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Springer Science and Business Media Deutschland GmbH
2018
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026662697&doi=10.1007%2f978-3-319-63859-1_46&partnerID=40&md5=4ced0da20f5776abff3e471bacd835ca http://eprints.utp.edu.my/21979/ |
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| Summary: | This paper proposes a Gaussian-Cauchy Particle Swarm Optimization (PSO) algorithm to provide the optimized parameters for a Feed Forward Neural Network. The improved PSO trains the Neural Network by optimizing the network weights and bias in the Neural Network. In comparison with the Back Propagation Neural Network, the Gaussian-Cauchy PSO Neural Network converges faster and is immune to local minima. © Springer International Publishing AG 2018. |
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