Title : ( A compact MLCP-based projection recurrent neural network model to solve shortest path problem )
Authors: mohammad eshaghnezhad , Sohrab Effati , Amin Mansoori ,
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Abstract
We develop a projection recurrent neural network (RNN) to obtain the solution of the shortest path problem (SPP). Our focus on the paper is to give a compact single-layer structure RNN model to solve the SPP. To present the RNN model, we utilise a mixed linear complementarity problem (MLCP). Moreover, the developed RNN is proved to be globally stable. Finally, some numerical simulations are stated to show the performance of the presented approach. We compare the results with some other methods.
Keywords
Shortest path problem; linear optimisation problem; recurrent neural network; globally stable
@article{paperid:1089651,
author = {Eshaghnezhad, Mohammad and Effati, Sohrab and Mansoori, Amin},
title = {A compact MLCP-based projection recurrent neural network model to solve shortest path problem},
journal = {Journal of Experimental and Theoretical Artificial Intelligence},
year = {2022},
volume = {35},
number = {7},
month = {April},
issn = {0952-813X},
pages = {1101--1119},
numpages = {18},
keywords = {Shortest path problem; linear
optimisation problem;
recurrent neural network;
globally stable},
}
%0 Journal Article
%T A compact MLCP-based projection recurrent neural network model to solve shortest path problem
%A Eshaghnezhad, Mohammad
%A Effati, Sohrab
%A Mansoori, Amin
%J Journal of Experimental and Theoretical Artificial Intelligence
%@ 0952-813X
%D 2022
