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Fuzzy Optimization and Decision Making

, Volume 17, Issue 1, pp 75–101 | Cite as

A neural network to solve quadratic programming problems with fuzzy parameters

  • Amin Mansoori
  • Sohrab Effati
  • Mohammad Eshaghnezhad
Article

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.

Keywords

Quadratic programming problem with fuzzy parameters Neural network model Fuzzy mapping Bi-objective problem Weighting problem 

Notes

Acknowledgements

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

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Amin Mansoori
    • 1
  • Sohrab Effati
    • 1
    • 2
  • Mohammad Eshaghnezhad
    • 1
  1. 1.Department of Applied MathematicsFerdowsi University of MashhadMashhadIran
  2. 2.Center of Excellence of Soft Computing and Intelligent Information Processing (SCIIP)Ferdowsi University of MashhadMashhadIran

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