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期刊论文
Training Radial Basis Function Networks with Particle Swarms
LNCS 3173, pp. 317-322, 2004，-0001，（）：
In this paper, Particle Swarm Optimization (PSO) algorithm, a new promising evolutionary algorithm, is proposed to train Radial Basis Function (RBF) network related to automatic configuration of network architecture. Classification tasks on data sets: Iris, Wine, Newthyroid, and Glass are conducted to measure the performance of neural networks. Compared with a standard RBF training algorithm in Matlab neural network toolbox, PSO achieves more rational architecture for RBF networks. The resulting networks hence obtain strong generalization abilities.
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