Abstract
When a radial-basis function neural network (RBFNN) is used for pattern classification, the problem involves designing the topology of RBFNN and also its centers and widths. In this paper, we present a particle swarm optimization (PSO) learning algorithm to automate the design of RBF networks, to solve pattern classification problems. Simulation results for benchmark problems in the pattern classification area show that the PSO-RBF outperforms two other learning algorithms in terms of network size and generalization performance.
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© 2005 Springer-Verlag Berlin Heidelberg
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Qin, Z., Chen, J., Liu, Y., Lu, J. (2005). Evolving RBF Neural Networks for Pattern Classification. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_142
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DOI: https://doi.org/10.1007/11596448_142
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
Online ISBN: 978-3-540-31599-5
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