Paper Info
Title
Projection Recurrent Neural Network Model: A New Strategy to Solve Maximum Flow Problem
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.
Year
DOI
Venue
2020
10.1109/TCSII.2020.2977862
IEEE Transactions on Circuits and Systems II: Express Briefs
Keywords
DocType
Volume
Maximum flow problem,projection recurrent neural network,Lyapunov stability
Journal
67
Issue
ISSN
Citations 
11
1549-7747
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Mohammad Eshaghnezhad1543.91
Effati Sohrab227630.31
F. Rahbarnia3124.06