Parametric NCP-Based Recurrent Neural Network Model: A New Strategy to Solve Fuzzy Nonconvex Optimization Problems

Abstract

The present scientific attempt is devoted to investigating the fuzzy nonconvex optimization problems (NCOPs) utilizing the concepts of recurrent neural networks (RNNs). To the best of our knowledge, this paper is the first study on finding a solution for fuzzy NCOP using RNN models. For this purpose, the original problem is reformulated into an mth power form, the interval, and then the weighting problem. Then, the Karush-Kuhn-Tucker (KKT) optimality conditions are provided for the weighting problem. The KKT conditions are used to propose the RNN model. Besides, the Lyapunov stability and the global convergence of the RNN model are proved. Finally, several illustrative examples are given to demonstrate the performance of this approach. The obtained results are compared with previous approaches for solving fuzzy NCOP.