A recurrent neural network for solving variational inequality problems with nonlinear constraints

Youshen Xia, Jun Wang

Research output: Contribution to journalConference articlepeer-review

Abstract

Variational inequalities with nonlinear inequality constraints are widely used in optimization and engineering problems. This paper present a recurrent neural network for solving variational inequalities with nonlinear inequality constraints in real time. The proposed neural network has one-layer structure and is amenable to parallel implementation. The proposed neural network is a significant generalization of several existing neural networks for optimization. Moreover, the proposed neural network is stable in the sense of Lyapunov and globally convergent to an optimal solution under a strictly monotone condition of the mapping. The simulation shows that the proposed neural network is effective for solving this class of variational inequality problems.

Original languageEnglish
Pages (from-to)207-210
Number of pages4
JournalIEEE International Conference on Neural Networks - Conference Proceedings
Volume1
Publication statusPublished - 2004
Externally publishedYes
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 25 Jul 200429 Jul 2004

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