A projection neural network for linear vector optimization problems
نویسنده:
, , , , ,سال
: 2018
چکیده: In the presence research, we are going to obtain the solution of the linear vector optimization problems. Our focus on the paper is to give a projection recurrent neural network model to solve the problem. Besides, the global convergence and the Lyapunov stability of system are proved in this research. Finally, a numerical simulations is given to show the performance of the proposed approach.
کلیدواژه(گان): Linear vector optimization problems,projection recurrent neural network,globally convergent,Lyapunov stability
کالکشن
:
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آمار بازدید
A projection neural network for linear vector optimization problems
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contributor author | امین منصوری | en |
contributor author | سهراب عفتی | en |
contributor author | محمد اسحاق نژاد | en |
contributor author | Amin Mansoori | fa |
contributor author | Sohrab Effati | fa |
contributor author | mohammad eshaghnezhad | fa |
date accessioned | 2020-06-06T14:30:17Z | |
date available | 2020-06-06T14:30:17Z | |
date copyright | 11/15/2018 | |
date issued | 2018 | |
identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/3399538 | |
description abstract | In the presence research, we are going to obtain the solution of the linear vector optimization problems. Our focus on the paper is to give a projection recurrent neural network model to solve the problem. Besides, the global convergence and the Lyapunov stability of system are proved in this research. Finally, a numerical simulations is given to show the performance of the proposed approach. | en |
language | English | |
title | A projection neural network for linear vector optimization problems | en |
type | Conference Paper | |
contenttype | External Fulltext | |
subject keywords | Linear vector optimization problems | en |
subject keywords | projection recurrent neural network | en |
subject keywords | globally convergent | en |
subject keywords | Lyapunov stability | en |
identifier link | https://profdoc.um.ac.ir/paper-abstract-1073006.html | |
identifier articleid | 1073006 |