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ISNN
2007
Springer

Recurrent Fuzzy CMAC for Nonlinear System Modeling

10 years 8 months ago
Recurrent Fuzzy CMAC for Nonlinear System Modeling
Normal fuzzy CMAC neural network performs well because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. In this paper, we use recurrent technique to overcome these problems and propose a new CMAC neural network, named recurrent fuzzy CMAC (RFCMAC). Since the structure of RFCMAC is more complex, normal training methods are difficult to be applied. A new simple algorithm with a time-varying learning rate is proposed to assure the learning algorithm is stable.
Floriberto Ortiz Rodriguez, Wen Yu, Marco A. Moren
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ISNN
Authors Floriberto Ortiz Rodriguez, Wen Yu, Marco A. Moreno-Armendariz, Xiaoou Li
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