A Novel Neural Network for Generally Constrained Variational Inequalities

Xingbao Gao, Lizhi LIAO

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

This paper presents a novel neural network for solving generally constrained variational inequality problems by constructing a system of double projection equations. By defining proper convex energy functions, the proposed neural network is proved to be stable in the sense of Lyapunov and converges to an exact solution of the original problem for any starting point under the weaker cocoercivity condition or the monotonicity condition of the gradient mapping on the linear equation set. Furthermore, two sufficient conditions are provided to ensure the stability of the proposed neural network for a special case. The proposed model overcomes some shortcomings of existing continuous-time neural networks for constrained variational inequality, and its stability only requires some monotonicity conditions of the underlying mapping and the concavity of nonlinear inequality constraints on the equation set. The validity and transient behavior of the proposed neural network are demonstrated by some simulation results.

Original languageEnglish
Article number7490354
Pages (from-to)2062-2075
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume28
Issue number9
DOIs
Publication statusPublished - Sep 2017

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • Convergence
  • neural network
  • nonmonotone problem
  • stability
  • variational inequality

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