Adaptive output-feedback stochastic nonlinear stabilization using neural network

被引：0
|
作者：
Yang, Jun [1 ]
Ni, Junchao [2 ]
Chen, Weisheng [3 ]
机构：
[1] Linyi Normal Coll, Dept Math, Linyi 276005, Peoples R China
[2] Shannxi Inst Educ, Dept Chem, Xian 710061, Peoples R China
[3] Xidian Univ, Dept Math Appl, Xian 710071, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2007, PT 1, PROCEEDINGS | 2007年 / 4491卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This letter extends adaptive neural network control method to a class of stochastic nonlinear output-feedback systems. Differently from the existing results, the nonlinear terms are assumed to be completely unknown and only a neural network is employed to compensate all unknown nonlinear functions. Based on stochastic LaSalle theorem, the resulting closed-loop system is proved to be globally asymptotically stable in probability.
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页码：158 / +
页数：2
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