A Neural Network for Constrained Fuzzy Convex Optimization Problems

Abstract

Fuzzy optimization widely occurs in various field. In this paper, by the virtue of weighting method, the original fuzzy optimization problem is eventually converted to a single-objective form. Then, a neurodynamic approach is introduced for this problem. The state solution of the neural network is shown to enter the feasible region of the considered optimization problem in finite time and remain in the feasible region since then. Moreover, the state solution of the introduced neural network converges to an optimal solution of the considered optimization problem. In the end, a numerical example is presented to clarify the practicability of the introduced neural network.