A projection neural network and its application to constrained optimization problems

Youshen Xia, Henry Leung, Jun Wang

Research output: Contribution to journalArticlepeer-review

258 Citations (Scopus)

Abstract

In this paper, we present a recurrent neural network for solving the nonlinear projection formulation. It is shown here that the proposed neural network is stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable, respectively under different conditions. Compared with the existing neural network for solving the projection formulation, the proposed neural network has a single-layer structure and is amenable to parallel implementation. Moreover, the proposed neural network has no Lipschitz condition, and, thus can be applied to solve a very broad class of constrained optimization problems that are special cases of the nonlinear projection formulation. Simulation shows that the proposed neural network is effective in solving these constrained optimization problems.

Original languageEnglish
Pages (from-to)447-458
Number of pages12
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Volume49
Issue number4
DOIs
Publication statusPublished - Apr 2002
Externally publishedYes

Keywords

  • Constrained optimization problems
  • Global stability
  • Recurrent neural network

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