进化编程优化RBF神经网络的结构和参数  

OPTIMIZE THE STRUCTURES AND PARAMETERS OF RBF NEURAL NETWORKS USING EVOLUTIONARY PROGRAMMING

作　　者：刘妹琴[1] 廖晓昕[1] 

机构地区：[1]华中理工大学控制科学与工程系,武汉430074

出　　处：《小型微型计算机系统》2000年第11期1182-1185,共4页Journal of Chinese Computer Systems

基　　金：国家自然科学基金资助项目! (项目号 6 98740 16 )

摘　　要：本文利用进化编程 (EP)来同时进化径向基函数神经网络 (RBFNN)的结构和参数 .与其它进化神经网络方法有以下四个方面的不同 :(1) EP是基于拉马克的进化学说 ,强调父代与子代之间的行为联接 ;(2 )进化算子中仅有突变 ,而没有交叉 ,以消除互换问题 ;(3)突变操作中 ,删除总是先于添加进行 ,以获得最简的网络结构 ;(4 )利用测试样本集构造适应度函数 ,以提高网络的泛化能力 .用进化 RBFNN来预测 Mackey- Glass时间序列的实验结果表明了该方法的有效性 .In this paper, the structures and parameters of radial basis function neural networks are evolved simultaneously by evolutionary programming(EP). There are four difference aspects between RBFNNs evolved by EP with other evolved neural networks: (1)EP based on Lamarckian evolution is put emphasis on the behavioral link between parents and their offspring; (2)In order to reduce the detrimental effect of the permutation problem, EP algorithm, which use mutation, rather than crossover, is adopted; (3)Node deletions are always attempted before node additions in the mutations in order to encourage the evolution of small RBFNNs; (4)The fitness function is determined through a test set. Such use of a test set improves the generalization ability of RBFNNs. The experimental results on evolved RBFNN predicting the Mackey Glass time series shows the new algorithm is very effective.

关 键 词：进化编程 K均值聚类法 RBF神经网络 优化 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]