Petri type 2 fuzzy neural networks approximator for adaptive control of uncertain non-linear systems

In this study, the authors developed a novel universal approximator by the integration of Petri networks into type 2 fuzzy neural networks (T2FNN). T2FNN involve large number of rules, which result in heavy computational burden and great computation time. By incorporating Petri layers to optimise th...

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Veröffentlicht in:	IET control theory & applications Jg. 11; H. 17; S. 3130 - 3136
Hauptverfasser:	Bibi, Youssouf, Bouhali, Omar, Bouktir, Tarek
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 24.11.2017
Schlagworte:	adaptive control approximation accuracy approximation theory closed loop systems closed‐loop system fuzzy neural nets fuzzy system fuzzy systems inverted pendulum system iterative K‐M inference procedure iterative methods Lyaponov approach Lyapunov methods nonlinear control systems pendulums Petri nets Petri type 2 fuzzy neural networks approximator PT2FNN approximator Research Article uncertain single‐input single‐output nonlinear system Lyaponov approach approximation theory iterative methods Petri type 2 fuzzy neural networks approximator PT2FNN approximator iterative K-M inference procedure approximation accuracy Petri nets pendulums fuzzy neural nets adaptive control closed loop systems nonlinear control systems fuzzy system fuzzy systems uncertain single-input single-output nonlinear system inverted pendulum system closed-loop system Lyapunov methods
ISSN:	1751-8644, 1751-8652
Online-Zugang:	Volltext
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