信息与控制2012，Vol.41Issue(2)：174-179,6.DOI:10.3724/SP.J.1219.2012.00174
一种基于量子粒子群的过程神经元网络学习算法
A Learning Algorithm of Process Neural Network Based on Quantum Particle Swarm
摘要
Abstract
Aiming al the problems of high computational complexity and convergence difficulty of the BP (backpropa-gation) algorithm based on orthogonal basis expansion because there are many parameters in the training of process neural network, a learning method of quantum particle swarm algorithm with double-chain structure is presented. The algorithm uses quantum bits to construct chromosomes. For the given model of process neural network, the number of genes on a chromosome is determined by the number of weight parameters and the population coding is completed. Individuals in the population are updated by quantum rotation gate and mutated by quantum non-gate. In the algorithm, each chromosome carries double chains of genes, which improves the possibility of optimums, expands the traverse of solution space and accelerates the optimization process of the process neural network. The process neural network trained by quantum particle swarm algorithm has been applied to Mackey-Glass time series and the sunspot prediction. The simulation results show that the algorithm not only has the fast convergence but also has good optimization ability.关键词
过程神经元网络/量子粒子群/网络训练/算法设计Key words
process neural network/ quantum particle swarm/ network training/ algorithm design分类
信息技术与安全科学引用本文复制引用
刘志刚,杜娟,李盼池..一种基于量子粒子群的过程神经元网络学习算法[J].信息与控制,2012,41(2):174-179,6.基金项目
中国博士后科学基金资助项目(20090460864) （20090460864）
黑龙江省教育厅科学技术研究资助项目(11551015). （11551015）
