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RBF neural network based on q-Gaussian function in function approximation |
Wei ZHAO( ), Ye SAN |
| 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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| Keywords
radial basis function (RBF) neural network
q-Gaussian function
particle swarm optimization algorithm
function approximation
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Corresponding Author(s):
ZHAO Wei,Email:zhaoweidaqing@yahoo.com.cn
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Issue Date: 05 December 2011
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