A recurrent neural network with exponential convergence for solving convex quadratic program and related linear piecewise equations.
This paper presents a recurrent neural network for solving strict convex quadratic programming problems and related linear piecewise equations. Compared with the existing neural networks for quadratic program, the proposed neural network has a one-layer structure with a low model complexity. Moreover, the proposed neural network is shown to have a finite-time convergence and exponential convergence. Illustrative examples further show the good performance of the proposed neural network in real-time applications.
Department of Manufacturing Engineering and Engineering Management, The City University of Hong Kong, Hong Kong, China. ysxia2001@yahoo.com
Youshen
Y
Xia
Gang
G
Feng
Jun
J
Wang
D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008959:Models, Neurological; D016571:Neural Networks, Computer; D012984:Software; D013997:Time Factors