基于单形进化的径向基网络训练算法

doi:10.3969/j.issn.0255-8297.2019.04.003

Abstract

Abstract: The control parameters of intelligent optimization algorithm have a great influence on the learning performance of intelligent optimization algorithm. In order to solve this problem, a radial neural network training algorithm based on simplex evolution is proposed. It uses a one-dimensional neighbor based full random search method to reduce the number of control parameters, maintain the particle diversity through the group multicolor state, and avoid the algorithm falling into the local extremum point. Simulation results show that the algorithm not only improves the recognition rate but also reduces the influence of control parameters on learning performance. The generalization and robustness of the algorithm are improved.

Key words: radial basis function (RBF) neural network, intelligent optimization, random search, evolutionary strategy, pattern recognition

CLC Number:

TP183

WEI Wei, QUAN Haiyan. Radial Basis Network Training Algorithm Based on Surface-Simplex Swarm Evolution[J]. Journal of Applied Sciences, 2019, 37(4): 459-468.

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Radial Basis Network Training Algorithm Based on Surface-Simplex Swarm Evolution

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