Title : ( Projection Recurrent Neural Network Model: A New Strategy to Solve Maximum Flow Problem )
Authors: mohammad eshaghnezhad , Sohrab Effati , Freydoon Rahbarnia ,
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Abstract
We study the maximum flow problem (MFP) employing the concepts of Recurrent Neural Networks (RNN)s. The aim of the present attempt is to find a solution for MFP utilizing projection RNN models based on mixed linear complementarity problem (MLCP). The Karush–Kuhn–Tucker (KKT) optimality conditions of the original problem are applied to develop the projection RNN model based on MLCP. Besides, the Lyapunov stability and the global convergence of the projection RNN model are proved. Finally, several illustrative examples are given to demonstrate the performance of this approach. The obtained results are compared with previous approaches to solving MFP
Keywords
, Maximum flow problem, projection recurrent neural network, Lyapunov stability.
@article{paperid:1079897,
author = {Eshaghnezhad, Mohammad and Effati, Sohrab and Rahbarnia, Freydoon},
title = {Projection Recurrent Neural Network Model: A New Strategy to Solve Maximum Flow Problem},
journal = {IEEE Transactions on Circuits and Systems Part II: Express Briefs},
year = {2020},
volume = {67},
number = {11},
month = {January},
issn = {1549-7747},
pages = {2747--2751},
numpages = {4},
keywords = {Maximum flow problem; projection recurrent neural network; Lyapunov stability.},
}
%0 Journal Article
%T Projection Recurrent Neural Network Model: A New Strategy to Solve Maximum Flow Problem
%A Eshaghnezhad, Mohammad
%A Effati, Sohrab
%A Rahbarnia, Freydoon
%J IEEE Transactions on Circuits and Systems Part II: Express Briefs
%@ 1549-7747
%D 2020
