计算机应用与软件2016，Vol.33Issue(11)：215-220,6.DOI:10.3969/j.issn.1000-386x.2016.11.051
基于群体划分优化的GAP-RBF神经网络学习算法
GAP-RBF NEURAL NETWORK LEARNING ALGORITHM BASED ON POPULATION PARTITIONING OPTIMISATION
摘要
Abstract
Aiming at the problem of traditional GAP-RBF algorithm that its learning accuracy is not high enough,we present in the paper a new GAP-RBF network learning algorithm which is based on population partitioning optimisation.First,for overcoming the large matrix computation problem in traditional GAP-RBF,the proposed algorithm adjusts network parameters with DEKF method;secondly,in order to obtain the network model with higher learning accuracy,the algorithm uses the PSO and GA-based population partitioning optimisation to train the connection weight values of hidden layers and output layers and the bias items.Experimental results indicate that compared with the algorithms such as RAN,RANEKF,MRAN and GAP-RBF,the presented algorithm can obtain a more concise network structure and improves the learning accuracy at the same time.关键词
径向基函数神经网络/增长剪枝径向基函数算法/粒子群优化算法/遗传算法Key words
RBF neural network/GAP-RBF algorithm/PSO algorithm/Genetic algorithm分类
信息技术与安全科学引用本文复制引用
包沁昕,宋威..基于群体划分优化的GAP-RBF神经网络学习算法[J].计算机应用与软件,2016,33(11):215-220,6.基金项目
物联网技术应用教育部工程研究中心，中央高校基本科研业务费专项资金项目（JUSRP51510）。 （）
