Title : ( An efficient neural network for solving convex optimization problems with a nonlinear complementarity problem function )
Authors: mahdi ranjbar taghi abad , Sohrab Effati , Seyed Mohsen Miri ,
دانلود فایل برای اعضای دانشگاهAccess to full-text not allowed by authors
Abstract
In this paper, we present a one-layer recurrent neural network (NN) for solving convex optimization problems by using the MangasarianandSolodov(MS)implicitLagrangianfunction.InthispaperbyusingKrush–Kuhn–TuckerconditionsandMS function the NN model was derived from an unconstrained minimization problem. The proposed NN model is one layer and compared to the available NNs for solving convex optimization problems, which has a better performance in convergence time. The proposed NN model is stable in the sense of Lyapunov and globally convergent to optimal solution of the original problem. Finally, simulation results on several numerical examples are presented and the validity of the proposed NN model is demonstrated.
Keywords
, One, layer neural networks·Convex programming·Nonlinear complementarity problem
@article{paperid:1075057,
author = {Ranjbar Taghi Abad, Mahdi and Effati, Sohrab and Miri, Seyed Mohsen},
title = {An efficient neural network for solving convex optimization problems with a nonlinear complementarity problem function},
journal = {Soft Computing},
year = {2019},
volume = {24},
number = {6},
month = {July},
issn = {1432-7643},
pages = {4233--4242},
numpages = {9},
keywords = {One-layer neural networks·Convex programming·Nonlinear complementarity problem},
}
%0 Journal Article
%T An efficient neural network for solving convex optimization problems with a nonlinear complementarity problem function
%A Ranjbar Taghi Abad, Mahdi
%A Effati, Sohrab
%A Miri, Seyed Mohsen
%J Soft Computing
%@ 1432-7643
%D 2019
