DocumentCode :
847811
Title :
A Recurrent Neural Network for Solving a Class of General Variational Inequalities
Author :
Hu, Xiaolin ; Wang, Jun
Author_Institution :
Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong
Volume :
37
Issue :
3
fYear :
2007
fDate :
6/1/2007 12:00:00 AM
Firstpage :
528
Lastpage :
539
Abstract :
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
Keywords :
mathematics computing; recurrent neural nets; variational techniques; asymptotic stability; general variational inequalities; recurrent neural network; Asymptotic stability; Control systems; Convergence; Equations; Iterative methods; Matrices; Neural networks; Pattern recognition; Quadratic programming; Recurrent neural networks; General projection neural network (GPNN); general variational inequalities (GVIs); global asymptotic stability; global exponential stability; recurrent neural network; Algorithms; Computer Simulation; Decision Support Techniques; Feedback; Mathematical Computing; Models, Theoretical; Neural Networks (Computer); Numerical Analysis, Computer-Assisted;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2006.886166
Filename :
4200801
Link To Document :
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