Title : ( Recurrent Neural Network Model: A New Strategy to Solve Fuzzy Matrix Games )
Authors: Amin Mansoori , mohammad eshaghnezhad , Sohrab Effati ,
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Abstract
This paper aims to investigate the fuzzy constrained matrix game -MG- problems using the concepts of recurrent neural networks -RNNs-. To the best of our knowledge, this paper is the first in attempting to find a solution for fuzzy game problems using RNN models. For this purpose, a fuzzy game problem is reformulated into a weighting problem. Then, the Karush–Kuhn–Tucker -KKT- optimality conditions are provided for the weighting problem. The KKT conditions are used to propose the RNN model. Moreover, the Lyapunov stability and the global convergence of the RNN model are also confirmed. Finally, three illustrative examples are presented to demonstrate the effectiveness of this approach. The obtained results are compared with the results obtained by the previous approaches for solving fuzzy constrained MG.
Keywords
, Fuzzy constrained matrix game -MG-, globally convergent, Lyapunov stability, neural network
@article{paperid:1072725,
author = {Mansoori, Amin and Eshaghnezhad, Mohammad and Effati, Sohrab},
title = {Recurrent Neural Network Model: A New Strategy to Solve Fuzzy Matrix Games},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
year = {2018},
volume = {50},
number = {1},
month = {December},
issn = {2162-237X},
pages = {1--10},
numpages = {9},
keywords = {Fuzzy constrained matrix game -MG-; globally
convergent; Lyapunov stability; neural network},
}
%0 Journal Article
%T Recurrent Neural Network Model: A New Strategy to Solve Fuzzy Matrix Games
%A Mansoori, Amin
%A Eshaghnezhad, Mohammad
%A Effati, Sohrab
%J IEEE Transactions on Neural Networks and Learning Systems
%@ 2162-237X
%D 2018
