A neural network to solve quadratic programming problems with fuzzy parameters
- 89 Downloads
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.
KeywordsQuadratic programming problem with fuzzy parameters Neural network model Fuzzy mapping Bi-objective problem Weighting problem
The authors wish to express our special thanks to the anonymous referees and editor for their valuable suggestions.
- Eshaghnezhad, M., Effati, S., & Mansoori, A. (2016). A neurodynamic model to solve nonlinear pseudo-monotone projection equation and its applications. IEEE Transactions on Cybernetics. doi: 10.1109/TCYB.2016.2611529.
- Khalil, H. K. (1996). Nonlinear systems. Michigan: prentice-hall.Google Scholar
- Mansoori, A., Effati, S., & Eshaghnezhad, M. (2016). An efficient recurrent neural network model for solving fuzzy non-linear programming problems. Applied Intelligence. doi: 10.1007/s10489-016-0837-4.
- Petersen, J. A. M., & Bodson, M. (2006). Constrained quadratic programming techniques for control allocation. IEEE Transactions on Control Systems Technology, 14(9), 1–8.Google Scholar