Resumen
In this paper, adaptive hierarchical fuzzy CMAC neural network controller (HFCMAC), for a certain class of nonlinear dynamical system is presented. The main advantages of adaptive HFCMAC control are: Better performance of the controller because adaptive HFCMAC can adjust itself to the changing enviroment and can be implemented in real time applications. The proposed method provides a simple control architecture that merges hierarchical structure, CMAC neural network and fuzzy logic. The input space dimension in CMAC is a time-consuming task especially when the number of inputs is huge this would be overload the memory and make the neuro-fuzzy system very hard to implement. This is can be simplified using a number of low-dimensional fuzzy CMAC in a hierarchical form. A new adaptation law is obtained for the method proposed, the overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Simulation results for its applications to one example is presented to demonstrate the performance of the proposed methodology. © 2008 IEEE.
Idioma original | Inglés estadounidense |
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Páginas | 294-304 |
Número de páginas | 263 |
DOI | |
Estado | Publicada - 24 dic. 2008 |
Evento | Proceedings - 2007 6th Mexican International Conference on Artificial Intelligence, Special Session, MICAI 2007 - Duración: 24 dic. 2008 → … |
Conferencia
Conferencia | Proceedings - 2007 6th Mexican International Conference on Artificial Intelligence, Special Session, MICAI 2007 |
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Período | 24/12/08 → … |