Title : ( On Shortest Path Problem via a Novel Neurodynamic Model: A Case Study )
Authors: Amin Mansoori , Sohrab Effati , mohammad eshaghnezhad ,
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Abstract
Our aim is investigating the Shortest Path Problem (SPP) by employing the Recurrent Neural Networks (RNNs). The Karush–Kuhn–Tucker (KKT) optimality conditions play an essential role to present the RNN model. In fact, the KKT conditions are reformulated as a Nonlinear Complementarity Problem (NCP). Indeed, the stability theorem of the RNN is provided. By testing some examples, we show the performance of the model. Also, we apply the approach to solve a real-world problem as a case study.
Keywords
, Shortest path problem, Linear optimization problem, Recurrent neural network, Lyapunov stability.
@article{paperid:1099988,
author = {Mansoori, Amin and Effati, Sohrab and Eshaghnezhad, Mohammad},
title = {On Shortest Path Problem via a Novel Neurodynamic Model: A Case Study},
journal = {Advances in Intelligent Systems and Computing},
year = {2021},
month = {January},
issn = {2194-5357},
keywords = {Shortest path problem; Linear optimization problem; Recurrent neural network; Lyapunov stability.},
}
%0 Journal Article
%T On Shortest Path Problem via a Novel Neurodynamic Model: A Case Study
%A Mansoori, Amin
%A Effati, Sohrab
%A Eshaghnezhad, Mohammad
%J Advances in Intelligent Systems and Computing
%@ 2194-5357
%D 2021
