Study on RBF NN Based on Improved Differential Evolution
A novel method of nonlinear system modeling using radial basis function neural network based on improved differential evolution algorithm is proposed. Differential evolution algorithm is presented to in order to improve modeling capability. Local operator and optimization selection strategy is presented to improve the searching speed and the local searching capability of genetic algorithm. According to the characteristics of radial basis function neural network and differential evolution algorithm, radial basis function neural network and differential evolution algorithm are associated to improve modeling precision. The simulation results show the effectiveness of this method.
Radial Basis Function Neural Network Improve Differential Evolution Algorithm Nonlinear System Local Operator
He dakuo Wang fuli Jia mingxing
Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, shenyang 110004
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
3508-3511
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）