title: A new one-layer neural network for linear and quadratic programming.
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.
authors.affiliation: College of Mathematics and Information Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, China. xinbaog@snnu.edu.cn
authors.forename: Xingbao
authors.initials: X
authors.lastname: Gao
authors.forename: Li-Zhi
authors.initials: LZ
authors.lastname: Liao
mesh_terms: D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011382:Programming, Linear