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A neural network to solve quadratic programming problems with fuzzy parameters

Author

Listed:
  • Amin Mansoori

    (Ferdowsi University of Mashhad)

  • Sohrab Effati

    (Ferdowsi University of Mashhad
    Ferdowsi University of Mashhad)

  • Mohammad Eshaghnezhad

    (Ferdowsi University of Mashhad)

Abstract

In this paper, a representation of a recurrent neural network to solve quadratic programming problems with fuzzy parameters (FQP) is given. The motivation of the paper is to design a new effective one-layer structure neural network model for solving the FQP. As far as we know, there is not a study for the neural network on the FQP. Here, we change the FQP to a bi-objective problem. Furthermore, the bi-objective problem is reduced to a weighting problem and then the Lagrangian dual is constructed. In addition, we consider a neural network model to solve the FQP. Finally, some illustrative examples are given to show the effectiveness of our proposed approach.

Suggested Citation

  • Amin Mansoori & Sohrab Effati & Mohammad Eshaghnezhad, 2018. "A neural network to solve quadratic programming problems with fuzzy parameters," Fuzzy Optimization and Decision Making, Springer, vol. 17(1), pages 75-101, March.
    • Handle: RePEc:spr:fuzodm:v:17:y:2018:i:1:d:10.1007_s10700-016-9261-9
    DOI: 10.1007/s10700-016-9261-9
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    References listed on IDEAS

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    1. Panigrahi, Motilal & Panda, Geetanjali & Nanda, Sudarsan, 2008. "Convex fuzzy mapping with differentiability and its application in fuzzy optimization," European Journal of Operational Research, Elsevier, vol. 185(1), pages 47-62, February.
    2. Abdel-Malek, Layek L. & Areeratchakul, Nathapol, 2007. "A quadratic programming approach to the multi-product newsvendor problem with side constraints," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1607-1619, February.
    3. Liu, Shiang-Tai, 2009. "A revisit to quadratic programming with fuzzy parameters," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1401-1407.
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