Global exponential stability of recurrent neural networks for solving optimization and related problems

Youshen Xia, Jun Wang

Research output: Contribution to journalArticlepeer-review

66 Citations (Scopus)

Abstract

Global exponential stability is a desirable property for dynamic systems. This paper studies the global exponential stability of several existing recurrent neural networks for solving linear programming problems, convex programming problems with interval constraints, convex programming problems with nonlinear constraints, and monotone variational inequalities. In contrast to the existing results on global exponential stability, the present results do not require additional conditions on the weight matrices of recurrent neural networks and improve some existing conditions for global exponential stability. Therefore, the stability results in this paper further demonstrate the superior convergence properties of the existing neural networks for optimization.

Original languageEnglish
Pages (from-to)1017-1022
Number of pages6
JournalIEEE Transactions on Neural Networks
Volume11
Issue number4
DOIs
Publication statusPublished - Jul 2000
Externally publishedYes

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