A recurrent neural network for solving a class of general variational inequalities

被引：68
|
作者：
Hu, Xiaolin [1 ]
Wang, Jun [1 ]
机构：
[1] Chinese Univ Hong Kong, Dept Automat & Comp Aided Engn, Shatin, Peoples R China
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2007年 / 37卷 / 03期
关键词：
general projection neural network (GPNN); general variational inequalities (GVIs); global asymptotic stability; global exponential stability; recurrent neural network; FUNCTIONAL-DIFFERENTIAL EQUATIONS; GLOBAL EXPONENTIAL STABILITY; PROJECTED DYNAMICAL-SYSTEMS; RATIO LIMIT THEOREMS; TIME-VARYING DELAYS; OPTIMIZATION PROBLEMS; CONSTRAINED OPTIMIZATION; DESCENT METHODS; CONVERGENCE;
D O I：
10.1109/TSMCB.2006.886166
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a recurrent neural-network model for solving a special class of general variational inequalities (GVIs), which includes classical VIs as special cases. It is proved that the proposed neural network (NN) for solving this class of GVIs can be globally convergent, globally asymptotically stable, and globally exponentially stable under different conditions. The proposed NN can be viewed as a modified version of the general projection NN existing in the literature. Several numerical examples are provided to demonstrate the effectiveness and performance of the proposed NN.
引用
收藏
页码：528 / 539
页数：12
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