工矿自动化2013，Vol.39Issue(9)：87-91,5.DOI:10.7526/j.issn.1671-251X.2013.09.023
并行混沌神经网络建模方法应用研究
Application research of modeling method based on neural networks with parallel chaotic search
摘要
Abstract
In view of nonlinear characteristic of switched reluctance motor and existing modeling method has shortcomings of random initial weights of network parameters and is easy to fall into local minimum point,the paper put forward a modeling method using parallel optimization chaotic and BP neural network.Firstly,the method uses chaotic system to optimize neural network weight vector and initial threshold vector,and then uses Levenberg-Marquardt algorithm of BP neural network to train convergence.If it drops into the local minimum point,then it needs to use parallel chaotic search to optimize model again,so as to make the model have characteristics of high precision and fast speed.The dynamic simulation results of training model and speed-regulation system of switched reluctance motor show that the model established by the method has stable operation,good dynamic performance,and fast response speed.关键词
开关磁阻电动机/调速系统/并行混沌搜索/BP神经网络/优化算法Key words
switched reluctance motor/ speed-regulation system/ parallel chaotic search/ BP neural network/ optimization algorithm分类
矿业与冶金引用本文复制引用
傅博娜,程勇..并行混沌神经网络建模方法应用研究[J].工矿自动化,2013,39(9):87-91,5.基金项目
西安科技大学培育基金项目(201211) （201211）
西安科技大学博士启动金项目(2013QDJ029). （2013QDJ029）
