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An efficient recurrent neural network model for solving fuzzy non-linear programming problems

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Abstract

In this paper, a representation of a recurrent neural network to solve fuzzy non-linear programming (FNLP) problems is given. The motivation of the paper is to design a new effective one-layer structure recurrent neural network model for solving the FNLP. Here, we change a fuzzy non-linear programming problem to a bi-objective problem. Furthermore, the bi-objective problem is reduced to a weighting problem and then the Lagrangian dual and the Karush-Kuhn-Tucker (KKT) optimality conditions are constructed. The simulation results on numerical examples are discussed to demonstrate the performance of our proposed approach.

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Acknowledgments

The authors wish to express our special thanks to the anonymous referees and editor for their valuable suggestions.

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Authors and Affiliations

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

    Amin Mansoori, Sohrab Effati & Mohammad Eshaghnezhad

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

    Sohrab Effati

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

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Mansoori, A., Effati, S. & Eshaghnezhad, M. An efficient recurrent neural network model for solving fuzzy non-linear programming problems. Appl Intell 46, 308–327 (2017). https://doi.org/10.1007/s10489-016-0837-4

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