计算机技术与发展2017，Vol.27Issue(10)：16-18,23,4.DOI:10.3969/j.issn.1673-629X.2017.10.004
基于聚粒子群算法的神经网络权值优化方法
A Neural Network Weights Optimization Method Based on Clustering Particle Swarm Optimization
摘要
Abstract
Neural network is a kind of excellent classification algorithm in the branch of machine intelligence,which has a wide range of applications in the field of image classification,face recognition and so on. However,because of its excessive parameters,it is easy to fall into the local optimal solution. According to this problem,a method combining particle swarm algorithm and clustering algorithm to opti-mize the weights of neural networks is proposed,which takes the neural network weights as the initial particle of particle swarm algorithm and uses the random of particle swarm algorithm to search the initial weights of neural network. Then the class contains more weight is found using C-means algorithm and its clustering center is regarded as the initial weights of BP neural network. The simulation results show that it is more excellent than the conventional particle swarm optimization algorithm in preventing the BP neural network from fall-ing into local optimum.关键词
BP神经网络/粒子群优化/聚类/权值/局部最优Key words
BP neural networks/particle swarm optimization/clustering/weight/local optimum分类
信息技术与安全科学引用本文复制引用
邓文杰..基于聚粒子群算法的神经网络权值优化方法[J].计算机技术与发展,2017,27(10):16-18,23,4.基金项目
国家自然科学基金资助项目(61571312) （61571312）
