Paper Info
Title
An efficient neurodynamic model to solve nonlinear programming problems with fuzzy parameters.
Abstract
In the present research, we obtain the solution of the fuzzy nonlinear programming problems (FNLPs) using recurrent neural network models. Since there is a few study and research for resolving of FNLP by recurrent neural network models, we obtain a new strategy to solve the problem. Reformulating the original problem to an interval program and then weighting problem, the Karush–Kuhn–Tucker (KKT) optimality conditions are presented. Moreover, we apply the KKT conditions into a recurrent neural network model as a high-performance tool to solve the problem. Besides, the global convergence and the Lyapunov stability of the system are proved in this research. In the final step, several illustrative examples are given to substantiate the obtained results. Reported results are compared with other published papers.
Year
DOI
Venue
2019
10.1016/j.neucom.2019.01.012
Neurocomputing
Keywords
Field
DocType
Nonlinear programming problems with fuzzy parameters,Biobjective program,Weighting program,Recurrent neural network,Lyapunov global stability,Globally convergent
Convergence (routing),Mathematical optimization,Weighting,Nonlinear programming,Fuzzy logic,Lyapunov stability,Recurrent neural network,Artificial intelligence,Karush–Kuhn–Tucker conditions,Mathematics,Machine learning
Journal
Volume
ISSN
Citations 
334
0925-2312
1
PageRank 
References 
Authors
0.35
31
2
Name
Order
Citations
PageRank
Amin Mansoori1585.31
Effati Sohrab227630.31