A General Fuzzified CMAC Neural Network and Its Simulation Study

Author

Shen, Zhipeng ; GUO, Chen ; Zhang, Xu

Author_Institution

Sch. of Autom. & Electr. Eng., Dalian Maritime Univ.

fYear

2005

fDate

27-29 June 2005

Firstpage

1251

Lastpage

1256

Abstract

Aiming at conventional cerebellar model articulation controller (CMAC), and combining CMAC addressing schemes with fuzzy logic idea, a general fuzzified CMAC (GFAC) is proposed, in which the fuzzy membership functions are utilized as the receptive field functions. The mapping of receptive field functions, the selection law of membership and the learning algorithm are presented in the paper. By using GFAC, the approximation of complex functions can be obtained which is more continuous than that by conventional CMAC. The simulation results show that GFAC has good generalization, comparatively high approximating accuracy, and ability to calculate function output differential

Keywords

cerebellar model arithmetic computers; fuzzy control; fuzzy logic; fuzzy set theory; generalisation (artificial intelligence); learning (artificial intelligence); neurocontrollers; cerebellar model articulation controller; function output differential; fuzzified CMAC neural network; fuzzy logic; fuzzy membership functions; generalization; learning algorithm; membership selection; receptive field functions; Automation; Biological neural networks; Brain modeling; Control systems; Function approximation; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation

Conference_Location

Limassol

ISSN

2158-9860

Print_ISBN

0-7803-8936-0

Type

conf

DOI

10.1109/.2005.1467195

Filename

1467195