Recurrent neural networks for solving linear inequalities and equations

Youshen Xia, Jun Wang, Donald L. Hung

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)

Abstract

This paper presents two types of recurrent neural networks, continuous-time and discrete-time ones, for solving linear inequality and equality systems. In addition to the basic continuous-time and discrete-time neural-network models, two improved discrete-time neural networks with faster convergence rate are proposed by use of scaling techniques. The proposed neural networks can solve a linear inequality and equality system, can solve a linear program and its dual simultaneously, and thus extend and modify existing neural networks for solving linear equations or inequalities. Rigorous proofs on the global convergence of the proposed neural networks are given. Digital realization of the proposed recurrent neural networks are also discussed.

Original languageEnglish
Pages (from-to)452-462
Number of pages11
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Volume46
Issue number4
DOIs
Publication statusPublished - 1999
Externally publishedYes

Keywords

  • Linear equalities and equations
  • Recurrent neural networks

Fingerprint

Dive into the research topics of 'Recurrent neural networks for solving linear inequalities and equations'. Together they form a unique fingerprint.

Cite this