A novel wavelet-based-CMAC neural network controller for nonlinear systems

Ching Hung Lee*, Bor Hang Wang, Hua Hsiang Chang, Yi Hung Pang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (CMAC NN) and develops a hybrid control scheme, combining supervisory controller, filter, and CMAC, for nonlinear systems. The Gaussian functions of traditional CMAC are replaced by wavelet functions. In addition, properties and advantages of fuzzy TSK- model are used to modify the activation functions of CMAC for obtaining high approximation accuracy and convergent rate. A PD type wavelet-based CMAC controller with pre-filter is constructed for nonlinear affine systems. The corresponding supervisory controller is used to compensate the wavelet-based CMAC controller for better performance. Several simulation results are shown to demonstrate the effectiveness of our approach.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
Pages2593-2599
Number of pages7
StatePublished - 2006
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Conference

ConferenceInternational Joint Conference on Neural Networks 2006, IJCNN '06
CountryCanada
CityVancouver, BC
Period16/07/0621/07/06

Keywords

  • CMAC neural network
  • Feedback
  • Nonlinear control
  • Wavelet

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