Title : ( An efficient recurrent neural network model for solving fuzzy non-linear programming problems )
Authors: Amin Mansoori , Sohrab Effati , mohammad eshaghnezhad ,
دانلود فایل برای اعضای دانشگاهAccess to full-text not allowed by authors
Abstract
In this paper, a representation of a recurrent neural network to solve fuzzy non-linear programming (FNLP) problems is given. The motivation of the paper is to design a new effective one-layer structure recurrent neural network model for solving the FNLP. Here, we change a fuzzy non-linear programming problem to a bi-objective problem. Furthermore, the bi-objective problem is reduced to a weighting problem and then the Lagrangian dual and the Karush-Kuhn-Tucker (KKT) optimality conditions are constructed. The simulation results on numerical examples are discussed to demonstrate the performance of our proposed approach
Keywords
, Fuzzy non, linear programming problems · Bi, objective problem · Weighting problem · Recurrent neural network · Globally stable in the sense of Lyapunov · Globally convergent
@article{paperid:1058183,
author = {Mansoori, Amin and Effati, Sohrab and Eshaghnezhad, Mohammad},
title = {An efficient recurrent neural network model for solving fuzzy non-linear programming problems},
journal = {Applied Intelligence},
year = {2016},
volume = {46},
number = {2},
month = {January},
issn = {0924-669X},
pages = {308--327},
numpages = {19},
keywords = {Fuzzy non-linear programming problems · Bi-objective problem · Weighting problem · Recurrent neural network · Globally stable in the sense of Lyapunov · Globally convergent},
}
%0 Journal Article
%T An efficient recurrent neural network model for solving fuzzy non-linear programming problems
%A Mansoori, Amin
%A Effati, Sohrab
%A Eshaghnezhad, Mohammad
%J Applied Intelligence
%@ 0924-669X
%D 2016
