微型机与应用2012，Vol.31Issue(14)：61-63,3.
一种基于改进遗传算法的径向基小波神经网络
A RBF wavelet neural network based on improved genetic algorithm
彭勇 1陈俞强 1严文杰2
作者信息
- 1. 东莞职业技术学院计算机工程系,广东东莞523808
- 2. 武汉理工大学计算机科学与技术学院,湖北武汉430070
- 折叠
摘要
Abstract
In order to improve the function fitting accuracy of the neural network,firstly, a four-layer RBF wavelet neural net- work is put forward on the basis of three-layer RBF neural network by increasing the level and setting the activation function to the wavelet function, and take genetic algorithm to determine contraction-expansion factor, size factor and weights of the initial net- work;secondly,aiming to premature convergence in genetic algorithm, dynamic equilibrium strategy is introduced to genetic algorithm, it dynamically change crossover and mutation probability in accordance with the variation of fitness to increase the equilibrium be- tween global exploratory and partial development. Finally,the function fitting experiment compared with other algorithms prove this method is better than others.关键词
径向基神经网络/小波神经网络/遗传算法/动态平衡/函数拟合Key words
RBF neural network/wavelet neural network/genetic algorithm/dynamic equilibrium/function fitting分类
信息技术与安全科学引用本文复制引用
彭勇,陈俞强,严文杰..一种基于改进遗传算法的径向基小波神经网络[J].微型机与应用,2012,31(14):61-63,3.
