A cooperative neural dynamic model for solving general convex nonlinear optimization problems with fuzzy parameters and an application in manufacturing systems

In the presented study, the solution of the fuzzy nonlinear optimization problems (FNLOPs) is calculated using a recurrent neural network (RNN) model. Since there is a few research for solving FNLOP by RNN's, we give a new approach to solve the problem. By reducing the original program to an in...

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Veröffentlicht in:International journal of adaptive control and signal processing Jg. 38; H. 6; S. 2272 - 2300
Hauptverfasser: Jahangiri, Mohammadreza, Nazemi, Alireza
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 01.06.2024
Schlagworte:
Dynamic models
Kuhn-Tucker method
Optimization
Recurrent neural networks
ISSN:0890-6327, 1099-1115
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Zusammenfassung:In the presented study, the solution of the fuzzy nonlinear optimization problems (FNLOPs) is calculated using a recurrent neural network (RNN) model. Since there is a few research for solving FNLOP by RNN's, we give a new approach to solve the problem. By reducing the original program to an interval problem and then weighting problem, the Karush–Kuhn–Tucker (KKT) conditions are given. Moreover, we use the KKT conditions into a RNN as an important tool to solve the problem. Besides, the global convergence properties and the Lyapunov stability of the dynamic model are studied in this study. In the final step, some illustrative examples are considered to establish the obtained results. Reported results are compared with some others network models.
Bibliographie:ObjectType-Article-1
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ISSN:0890-6327
1099-1115
DOI:10.1002/acs.3804