基于混沌神经网络的异步电主轴转速辨识  

Electro-spindle speed identifier based on chaos-BP algorithm

作　　者：杨炎[1] 孙首群[1] 吴虹江[1] 崔熠[1] 

机构地区：[1]上海理工大学机械工程学院,上海200093

出　　处：《现代制造工程》2011年第12期18-23,共6页Modern Manufacturing Engineering

基　　金：国家863计划资助项目(2009AA11Z211);上海市教委重点学科建设项目(J50503)

摘　　要：针对BP神经网络易陷于局部最优,且学习速度慢的缺陷,研究了采用混沌优化算法改进BP神经网络并应用于异步电主轴转速辨识的方法。该方法使用新的混沌自映射函数取代一般的有限折叠次数的Logistic自映射函数,使其兼具了混沌优化算法的全局寻优与BP算法局部寻优的优点。借助MATLAB/Simulink软件对无传感器电主轴转速辨识系统进行数值仿真。仿真结果表明,采用新方法使整个系统的辨识性能更优,学习速度更快。BP neural network is easy to plunge into local solution and has slow learning convergence speed,a new chaos optimization algorithm was investigated. A new chaotic self-map is applied to replace the Logistic chaotic self-map with finite collapse and it realized the combination of chaos＇ global search capability and BP＇s local optimize performance. The new algorithm applied in electro-spindle was carried out by simulation experiment using MATLAB/simulink. The numerical results show that speed identification system has not only the advantage of accurate identification, but also the virtue of quick learning convergence speed adopted the new algorithm.

关 键 词：转速辨识 混沌优化算法 BP神经网络 异步电主轴 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]