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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Wei Zhao received his Masters degree from Harbin Engineering University in 2007. He is a PhD candidate at the Harbin Institute of Technology, China. His research interests include quantum genetic algorithms, quantum-behaved particle swarm algorithms, and neural networks.
Ye San is a professor and PhD advisor at the Harbin Institute of Technology. He received his bachelor degree from Harbin Institute of Technology, China, in 1976. His research interests include modeling, simulation and optimization of complex systems.
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Zhao, W., San, Y. RBF neural network based on q-Gaussian function in function approximation. Front. Comput. Sci. China 5, 381–386 (2011). https://doi.org/10.1007/s11704-011-1041-7
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DOI: https://doi.org/10.1007/s11704-011-1041-7