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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Evolving RBF Neural Networks for Pattern Classification
verfasst von : Zheng Qin, Junying Chen, Yu Liu, Jiang Lu
Erschienen in: Computational Intelligence and Security
Verlag: Springer Berlin Heidelberg
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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.