Recurrent neural networks for soving linear inequalities and equations

Youshen Xia; Jun Wang; Donald L. Hung

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Recurrent neural networks for soving linear inequalities and equations

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摘要

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 improveddiscrete-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 extendand 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.

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《IEEE Transactions on Automatic Control》 |1999年第4期|452-462|共11页
作者
Youshen Xia; Jun Wang; Donald L. Hung;
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原文格式 PDF
正文语种英语
中图分类自动化系统;
关键词
Linear Equalities and equations; Recurrent neural networks;

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Recurrent neural networks for soving linear inequalities and equations

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