Cluster Distance Factor Searching by Particle Swarm Optimization for Self-Growing Radial Basis Function Neural Network

A very important step for the RBF network training is to decide a proper number of hidden nodes. This paper proposes a PSO-based algorithm for searching optimal cluster distance factor. Thus, the self-growing RBF network training algorithm can be realized by employing the optimal cluster distance factor to create hidden neurons automatically from the input data set. Two experiments that include function approximation and chaotic time series prediction are used to compare our proposed method with other related approaches. The PSO-based RBF network exhibits better generalization performance and shorter training time.

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