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This paper presents a model selection method for normalized Gaussian network (NGnet). We introduce a hierarchical prior distribution of the model parameters and the NGnet is trained based on the variational Bayes (VB) inference. The free energy calculated in the VB inference is used as a criterion for the model selection. In order to efficiently search for the optimal model structure, we develop a hierarchical model selection method. The performance of our method is evaluated by using function approximation and nonlinear dynamical system identification problems. Our method achieved better performance than existing methods.

Normalized Gaussian Network Based on Variational Bayes Inference and Hierarchical Model Selection