| 基于改进差分进化算法的RBF神经网络优化方法 |
| |
| 引用本文： | 方力智,张翠芳,易芳.基于改进差分进化算法的RBF神经网络优化方法[J].成都大学学报(自然科学版),2009,28(3):231-233,239. |
| |
| 作者姓名： | 方力智 张翠芳 易芳 |
| |
| 作者单位： | 西南交通大学,信息科学与技术学院,四川,成都,610031 |
| |
| 摘 要： | 提出了一种新的RBF神经网络训练方法——改进差分进化算法,并用改进差分进化优化的神经网络对非线性系统进行逼近.采用改进差分进化算法对RBF神经网络的中心值、宽度和权值进行了优化.仿真实验结果表明,改进的差分进化算法具有比遗传算法更强的非线性系统逼近能力.
|
| 关 键 词： | 改进差分进化算法 径向基函数神经网络 非线性系统逼近 |
Optimization Approach Based on Modified Differential Evolution Algorithm for RBF Neural Network |
| |
| FANG Lizhi,ZHANG Cuifang,YI Fang.Optimization Approach Based on Modified Differential Evolution Algorithm for RBF Neural Network[J].Journal of Chengdu University (Natural Science),2009,28(3):231-233,239. |
| |
| Authors: | FANG Lizhi ZHANG Cuifang YI Fang |
| |
| Institution: | (School of Information Science and Technique, Southwest Jiaotong University, Chengdu 610031, China) |
| |
| Abstract: | A new training method of RBF neural network based on modified differential evolution(MDE) was proposed and applied to the nonlinear system approximation for RBF neural network.Modified differential evolution algorithm was used to optimize the centers,widths and weights of RBF.The experiment results show that the MDE algorithm has better capability for approximating nonlinear system than the genetic algorithm. |
| |
| Keywords: | modified differential evolution algorithm radial basis function neural network approximation of nonlinear system |
| 本文献已被 维普 万方数据 等数据库收录！ |
