Abstract
This paper develops a RBF neural network based on particle swarm optimization (PSO) algorithm. It is composed of a RBF neural network, whose parameters including clustering centers, variances of Radial Basis Function and weights are optimized by PSO algorithm. Therefore it has not only simplified the structure of RBF neural network, but also enhanced training speed and mapping accurate. The performance and effectiveness of the proposed method are evaluated by using function simulation and compared with RBF neural network. The result shows that the optimized RBF neural network has significant advantages inspect of fast convergence speed, good generalization ability and not easy to yield minimal local results.
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Shao, Y., Chen, Q., Jiang, H. (2010). RBF Neural Network Based on Particle Swarm Optimization. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_22
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DOI: https://doi.org/10.1007/978-3-642-13278-0_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13277-3
Online ISBN: 978-3-642-13278-0
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