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

Xiaolin Hu, Jun Wang

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)528-539
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume37
Issue number3
DOIs
Publication statusPublished - Jun 2007
Externally publishedYes

Keywords

  • General projection neural network (GPNN)
  • General variational inequalities (GVIs)
  • Global asymptotic stability
  • Global exponential stability
  • Recurrent neural network

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