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

Authors:
Xingbao Gao;Li-Zhi Liao
Affiliations:
College of Mathematics and Information Science, Shaanxi Normal University, Xi'an, Shaanxi, China;Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, China
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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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.