TY - GEN
T1 - Recurrent fuzzy CMAC for nonlinear system modeling
AU - Ortiz, Floriberto
AU - Yu, Wen
AU - Moreno-Armendariz, Marco
AU - Li, Xiaoou
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=37249006779&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-72383-7_58
DO - 10.1007/978-3-540-72383-7_58
M3 - Contribución a la conferencia
SN - 9783540723820
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 487
EP - 495
BT - Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings
PB - Springer Verlag
T2 - 4th International Symposium on Neural Networks, ISNN 2007
Y2 - 3 June 2007 through 7 June 2007
ER -