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

被引：17
作者：
Mansoori, Amin [1 ]
Effati, Sohrab [1 ]
机构：
[1] Ferdowsi Univ Mashhad, Dept Appl Math, Mashhad 9177948974, Razavi Khorasan, Iran
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2021年 / 51卷 / 04期
关键词：
Recurrent neural networks; Lyapunov methods; Mathematical model; Lagrangian functions; Fuzzy set theory; Pareto optimization; Bi-objective and weighting programs; fuzzy nonconvex optimization problem (NCOP); global Lyapunov stability; NCP function; recurrent neural network (RNN);
D O I：
10.1109/TSMC.2019.2916750
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：2592 / 2601
页数：10
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