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A Feedback Neural Network for Solving Nonlinear Programming Problems with Hybrid Constraints

by Hamid Reza Vahabi, Hasan Ghasabi-oskoei
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 54 - Number 5
Year of Publication: 2012
Authors: Hamid Reza Vahabi, Hasan Ghasabi-oskoei
10.5120/8565-2164

Hamid Reza Vahabi, Hasan Ghasabi-oskoei . A Feedback Neural Network for Solving Nonlinear Programming Problems with Hybrid Constraints. International Journal of Computer Applications. 54, 5 ( September 2012), 41-46. DOI=10.5120/8565-2164

@article{ 10.5120/8565-2164,
author = { Hamid Reza Vahabi, Hasan Ghasabi-oskoei },
title = { A Feedback Neural Network for Solving Nonlinear Programming Problems with Hybrid Constraints },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 54 },
number = { 5 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 41-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume54/number5/8565-2164/ },
doi = { 10.5120/8565-2164 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:56:03.672745+05:30
%A Hamid Reza Vahabi
%A Hasan Ghasabi-oskoei
%T A Feedback Neural Network for Solving Nonlinear Programming Problems with Hybrid Constraints
%J International Journal of Computer Applications
%@ 0975-8887
%V 54
%N 5
%P 41-46
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a high-performance feedback neural network model for solving nonlinear convex programming problems with hybrid constraints in real time by means of the projection method. In contrary to the existing neural networks, this general model can operate not only on bound constraints, but also on hybrid constraints comprised of inequality and equality constraints. It is shown that the proposed neural network is stable in the sense of Lyapunov and can be globally convergent to an exact optimal solution of the original problem under some weaker conditions. Moreover, it has a simpler structure and a lower complexity. The advanced performance of the proposed neural network is demonstrated by simulation of several numerical examples.

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Index Terms

Computer Science
Information Sciences

Keywords

Nonlinear programming Feedback neural network Global convergence and stability

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