Title : ( A capable neural network model for fuzzy quadratic optimization problems )
Authors: Amin Mansoori , Sohrab Effati ,
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Abstract
In this paper, a representation of a recurrent neural network to solve fuzzy quadratic programming problems -FQP- is given. The motivation of the paper is to design a new effective one-layer structure recurrent neural network model for solving the FQP. Here, we reformulate the FQP to a bi-objective problem. Furthermore, the bi-objective problem is reduced to a weighting problem and then the KarushKuhn-Tucker -KKT- optimality conditions of the problem are constructed. A novel recurrent neural network model to solve the FQP is developed. Finally, an illustrative example is presented.
Keywords
, Fuzzy quadratic programming problem, recurrent neural network model, bi-objective problem, weighting problem, globally stable in the sense of Lyapunov.
@inproceedings{paperid:1073007,
author = {Mansoori, Amin and Effati, Sohrab},
title = {A capable neural network model for fuzzy quadratic optimization problems},
booktitle = {هشتمین سمینار آمار و احتمال فازی},
year = {2018},
location = {مشهد, IRAN},
keywords = {Fuzzy quadratic programming problem; recurrent neural network model; bi-objective problem; weighting problem; globally stable in the sense of Lyapunov.},
}
%0 Conference Proceedings
%T A capable neural network model for fuzzy quadratic optimization problems
%A Mansoori, Amin
%A Effati, Sohrab
%J هشتمین سمینار آمار و احتمال فازی
%D 2018
