计算机工程与应用2012，Vol.48Issue(20)：146-149,157,5.DOI:10.3778/j.issn.1002-8331.2012.20.030
一种改进的RBF神经网络参数优化方法
Improved method for RBF neural network parameters optimization
摘要
Abstract
An improved method for RBF neural network parameters optimization is proposed. The number of nodes in the hidden layer is determined by using RAN (Resource Allocating Network), meanwhile strategy of pruning is introduced to remove those hidden units which make insignificant contribution to overall network output. Central position, width and weight of the neural network are optimized by the improved PSO (Particle Swarm Optimization) algorithm, so as to obtain the appropriate structure and control parameters. The new algorithm is used to predict the model of CSTR, and the result indicates that RBF neural network optimized by this algorithm has a smaller structure and high generalization ability.关键词
径向基神经网络/资源分配网络/剪枝策略/粒子群优化Key words
radial basis function neural network/ resource allocating network/ strategy of pruning/ particle swarm分类
信息技术与安全科学引用本文复制引用
张辉,柴毅..一种改进的RBF神经网络参数优化方法[J].计算机工程与应用,2012,48(20):146-149,157,5.基金项目
国家自然科学基金(No.60974090) （No.60974090）
重庆市科技攻关资助项目(No.cstc2010ac3055) （No.cstc2010ac3055）
中央高校基本科研业务专项经费(No.CDJXS11172237). （No.CDJXS11172237）
