One-layer continuous-and discrete-time projection neural networks for solving variational inequalities and related optimization problems

Qingshan Liu, Tingwen Huang, Jun Wang

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)

Abstract

This paper presents one-layer projection neural networks based on projection operators for solving constrained variational inequalities and related optimization problems. Sufficient conditions for global convergence of the proposed neural networks are provided based on Lyapunov stability. Compared with the existing neural networks for variational inequalities and optimization, the proposed neural networks have lower model complexities. In addition, some improved criteria for global convergence are given. Compared with our previous work, a design parameter has been added in the projection neural network models, and it results in some improved performance. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural networks.

Original languageEnglish
Article number6680760
Pages (from-to)1308-1318
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number7
DOIs
Publication statusPublished - Jul 2014
Externally publishedYes

Keywords

  • Constrained optimization
  • global convergence
  • Lyapunov stability
  • projection neural network
  • variational inequalities

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