Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

A new one-layer neural network for linear and quadratic programming

Language

English

Abstract

In this paper, we present a new neural network for solving linear and quadratic programming problems in real time by introducing some new vectors. The proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem when the objective function is convex on the set defined by equality constraints. Compared with existing one-layer neural networks for quadratic programming problems, the proposed neural network has the least neurons and requires weak stability conditions. The validity and transient behavior of the proposed neural network are demonstrated by some simulation results. © 2006 IEEE.

Keywords

Convergence, Linear and quadratic programming, Neural network, Stability

Publication Date

2010

Source Publication Title

IEEE Transactions on Neural Networks

Volume

21

Issue

6

Start Page

918

End Page

929

Publisher

Institute of Electrical and Electronics Engineers

DOI

10.1109/TNN.2010.2045129

Link to Publisher's Edition

http://dx.doi.org/10.1109/TNN.2010.2045129

ISSN (print)

10459227

ISSN (electronic)

19410093

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