Application of an Improved RBF Neural Network in Sliding Mode Control System

摘要：

Equivalent sliding mode control based on RBF neural network uses the traditional gradient descent algorithm to achieve the control function.Because of local minima, training is slow and so on. The algorithm has slow convergence, poor adaptability problems.This paper presents a RBF network based on variable learning rate of W which can be used to equivalent sliding mode control system.Experimental results of the simulation show that the new algorithm has fast convergence and tracking precision. It can effectively avoid the interference caused by unknown divergence, and have a good control of reliability.

关键词： gradient descent algorithm RBF network variable learning rate equivalent sliding mode control

作者: ZHANG Yan-jun LIU Yao-da

作者单位: Qingdao University of Science and Technology College of Automation and Electronic Engineering Qingdao, Shandong, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 489-492

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）