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Modelling, control, and stability analysis of non-linear systems using generalized fuzzy neural networks.
- Source :
-
International Journal of Systems Science . 5/15/2003, Vol. 34 Issue 6, p427-438. 12p. - Publication Year :
- 2003
-
Abstract
- This paper presents an adaptive fuzzy neural controller (AFNC) suitable for modelling and control of MIMO non-linear dynamic systems. The proposed AFNC has the following salient features: (1) fuzzy neural control rules can be generated or deleted dynamically and automatically; (2) uncertain MIMO non-linear systems can be adaptively modelled on line; (3) adaptation and learning speed is fast; (4) expert knowledge can be easily incorporated into the system; (5) the structure and parameters of the AFNC can be self-adaptive in the presence of uncertainties to maintain a high control performance; and (6) the asymptotical stability of the system is established using the Lyapunov approach. Simulation studies on a two-link robot manipulator show that the performance of the proposed controller is better than that of some existing fuzzy/neural methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- *NONLINEAR systems
*LYAPUNOV functions
*FUZZY systems
*ARTIFICIAL intelligence
Subjects
Details
- Language :
- English
- ISSN :
- 00207721
- Volume :
- 34
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- International Journal of Systems Science
- Publication Type :
- Academic Journal
- Accession number :
- 11622824
- Full Text :
- https://doi.org/10.1080/00207720310001612855