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

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.