Paper Info
Title
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.
Year
DOI
Venue
2021
10.1109/TSMC.2019.2916750
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Bi-objective and weighting programs,fuzzy nonconvex optimization problem (NCOP),global Lyapunov stability,NCP function,recurrent neural network (RNN)
Journal
51
Issue
ISSN
Citations 
4
2168-2216
1
PageRank 
References 
Authors
0.34
20
2
Name
Order
Citations
PageRank
Amin Mansoori1585.31
Effati Sohrab227630.31