Volume 16, Issue 3 (10-2019)                   jor 2019, 16(3): 1-20 | Back to browse issues page

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Nazemi A, Sukhtsaraee S, Mortezaee M. An efficient modified neural network for solving nonlinear programming problems with hybrid constraints. jor 2019; 16 (3) :1-20
URL: http://jamlu.liau.ac.ir/article-1-1632-en.html
An efficient modified neural network for solving nonlinear programming problems with hybrid constraints
A. Nazemi , S. Sukhtsaraee , M. Mortezaee *
Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
Abstract:   (3342 Views)
This paper presents ‎‎the optimization techniques for solving‎‎ convex programming problems with hybrid constraints‎.‎ According to the saddle point theorem‎, ‎optimization theory‎, ‎convex analysis theory‎, ‎Lyapunov stability theory and LaSalle‎‎invariance principle‎,‎ a neural network model is constructed‎.‎ The equilibrium point of the proposed model is proved to be equivalent to the optimal ‎‎solution of the original problem‎. ‎It is also shown that the proposed network model is stable in the Lyapunov sense and it is globally convergent to an exact optimal solution of the original problem‎. ‎Several practical examples are provided to show the feasibility and the efficiency of the ‎method.
Keywords: Convex programming, Lyapunov stability, Hybrid constraints, Neural network, Globally convergent
Full-Text [PDF 1079 kb]   (1106 Downloads)    
Type of Study: Research | Subject: Special
Received: 2018/02/6 | Accepted: 2019/03/15 | Published: 2019/10/2
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