Recurrent fuzzy CMAC in hierarchical form for dynamic system identification

Floriberto Ortiz Rodriguez, Wen Yu, Marco A. Moreno-Armendariz

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

The conventional fuzzy CMAC neural networks perform well in terms of their fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires an enormous memory and the dimension increase exponentially with the input number. In this paper, we use two techniques to overcome these problems: recurrent and hierarchical structures and propose a new CMAC, named Hierarchical Recurrent Fuzzy CMAC (HRFCMAC). Since the structure of HRFCMAC is very complex, the normal training methods are difficult to be applied. A new simple algorithm is given, we can train each sub-block of the hierarchical CMAC independently. A time-varying learning rate assures the learning algorithm is stable.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2007 American Control Conference, ACC
Páginas5706-5711
Número de páginas6
DOI
EstadoPublicada - 2007
Evento2007 American Control Conference, ACC - New York, NY, Estados Unidos
Duración: 9 jul. 200713 jul. 2007

Serie de la publicación

NombreProceedings of the American Control Conference
ISSN (versión impresa)0743-1619

Conferencia

Conferencia2007 American Control Conference, ACC
País/TerritorioEstados Unidos
CiudadNew York, NY
Período9/07/0713/07/07

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