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An efficient recurrent neural network model for solving fuzzy non-linear programming problems
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DOI：10.1007/s10489-016-0837-4.png)
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Abstract
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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.
Keywords:
Fuzzy non-linear programming problems
Bi-objective problem
Weighting problem
Recurrent neural network
Globally stable in the sense of Lyapunov
Globally convergent
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