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An Artificial Neural Network Model to Solve the Fuzzy Shortest Path Problem

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Abstract

In the present research, we are going to obtain the solution of the fuzzy shortest path (FSP) problem. According to our search in the scientific reported papers, this is the first scientific attempt for resolving of FSP by artificial neural network model which has the global exponential stability property. Here, by reformulating the original problem to an interval program and then weighting problem, the Karush–Kuhn–Tucker (KKT) optimality conditions are suggested as a new strategy for the problem. Moreover, we apply the KKT conditions to proposed an artificial neural network model as a high-performance tool to provide the solution of the problem. In continuous, the global convergence and the global exponential stability of the model were approved in this research. In the final step, several numerical examples are presented to depict the performance of the method. Reported results were compared with some other published papers.

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Authors and Affiliations

  1. Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran

    Mohammad Eshaghnezhad, Freydoon Rahbarnia, Sohrab Effati & Amin Mansoori

  2. Center of Excellence of Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran

    Sohrab Effati

Authors
  1. Mohammad Eshaghnezhad
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  2. Freydoon Rahbarnia
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  3. Sohrab Effati
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  4. Amin Mansoori
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Correspondence to Freydoon Rahbarnia.

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Eshaghnezhad, M., Rahbarnia, F., Effati, S. et al. An Artificial Neural Network Model to Solve the Fuzzy Shortest Path Problem. Neural Process Lett 50, 1527–1548 (2019). https://doi.org/10.1007/s11063-018-9945-y

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