首页 | 本学科首页   官方微博 | 高级检索  
     

非最小相位非线性系统的简单递归神经网络控制
引用本文:李 翔,陈增强,袁著祉.非最小相位非线性系统的简单递归神经网络控制[J].控制理论与应用,2001,18(3):456-460.
作者姓名:李 翔  陈增强  袁著祉
作者单位:南开大学自动化系
基金项目:Foundation item:supported by National 863 CIMS Project Foundation (863-511-945-010), Natural Science Foundation of Tianjin (983602011) and University Key Teacher Foundation of Ministry of Education (0065).
摘    要:从简单递归神经网络的统一结构出发设计了简单递归神经网络控制器,在引入了控制加权的目标函数下优化神经网络权值学习,因此是通常意义的神经网络控制的推广。证明了整个系统的稳定性,并通过仿真验证了控制器的有效性。

关 键 词:简单递归神经网络  非最小相位系统  非线性系统  控制器
文章编号:1000-8152(2001)03-0456-05
收稿时间:7/7/2000 12:00:00 AM
修稿时间:2000年7月7日

Simple Recurrent Neural Network Control for Non-minimum Phase Nonlinear System
LI Xiang,CHEN Zeng-qiang and YUAN Zhu-zhi.Simple Recurrent Neural Network Control for Non-minimum Phase Nonlinear System[J].Control Theory & Applications,2001,18(3):456-460.
Authors:LI Xiang  CHEN Zeng-qiang and YUAN Zhu-zhi
Affiliation:Department of Automation, Nankai University, Tianjin, 300071,P.R.China;Department of Automation, Nankai University, Tianjin, 300071,P.R.China;Department of Automation, Nankai University, Tianjin, 300071,P.R.China
Abstract:We discuss a uniform structure of simple recurrent neural networks, based on which a novel neural control system is developed. With the introduction of the weighted control information into the neural controller's cost function, the method is an extension of the common neural networks controller proposed before. The stability of the whole neural control system is demonstrated and its effectiveness is verified via simulation.
Keywords:simple recurrent neural networks  non  minimum phase system  nonlinear system  neural network control
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《控制理论与应用》浏览原始摘要信息
点击此处可从《控制理论与应用》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号