Title of article
A Recurrent Neural Network for Solving Strictly Convex Quadratic Programming Problems
Author/Authors
Ghomashi, A. Department of Mathematics - Kermanshah Branch, Islamic Azad University, Kermanshah, Iran , Abbasi, M. Department of Mathematics - Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
Pages
9
From page
339
To page
347
Abstract
In this paper we present an improved neural network to solve strictly convex quadratic programming(QP) problem. The proposed model is derived based on a piecewise equation correspond to optimality condition of convex (QP) problem and has a lower structure complexity respect to the other existing neural network model for solving such problems. In theoretical aspect, stability and global convergence of the proposed neural network is proved.
Keywords
Recurrent neural network , Dynamical system , Strictly convex quadratic programming , Global convergence , stability
Journal title
Astroparticle Physics
Serial Year
2018
Record number
2438575
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