RBF neural network based on q-Gaussian function in function approximation |
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Authors: | Wei Zhao Ye San |
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Affiliation: | 1. Control and Simulation Center, Harbin Institute of Technology, Harbin, 150001, China
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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. |
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