A Recurrent Neural Network for Solving Strictly Convex Quadratic Programming Problems
Publish Year: 1397
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJIM-10-4_003
تاریخ نمایه سازی: 26 دی 1402
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:
Dynamical system , Strictly convex quadratic programming , Stability , Global convergence , Recurrent neural network
Authors
A. Ghomashi
Department of Mathematics, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
M. Abbasi
Department of Mathematics, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.