RBF neural network based on q-Gaussian function in function approximation

Authors:
Wei Zhao;Ye San
Affiliations:
Control and Simulation Center, Harbin Institute of Technology, Harbin, China 150001;Control and Simulation Center, Harbin Institute of Technology, Harbin, China 150001
Venue:
Frontiers of Computer Science in China
Year:
2011

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Abstract

To enhance the generalization performance of radial basis function (RBF) neural networks, an RBF neural network based on a q-Gaussian function is proposed. A q-Gaussian function is chosen as the radial basis function of the RBF neural network, and a particle swarm optimization algorithm is employed to select the parameters of the network. The non-extensive entropic index q is encoded in the particle and adjusted adaptively in the evolutionary process of population. Simulation results of the function approximation indicate that an RBF neural network based on q-Gaussian function achieves the best generalization performance.