Automatic design of hierarchical RBF networks for system identification

The purpose of this study is to identify the hierarchical radial basis function neural networks and select important input features for each sub-RBF neural network automatically. Based on the pre-defined instruction/operator sets, a hierarchical RBF neural network is created and evolved by using Extended Compact Genetic Programming (ECGP), and the parameters are optimized by Differential Evolution (DE) algorithm. Empirical results on benchmark system identification problems indicate that the proposed method is efficient.

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