A recurrent interval type-2 fuzzy neural network with asymmetric membership functions for nonlinear system identification

Ching Hung Lee*, Tzu Wei Hu, Chung Ta Lee, Yu Chia Lee

*Corresponding author for this work

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

28 Scopus citations

Abstract

This paper proposes a recurrent interval type-2 fuzzy neural network with asymmetric membership functions (RT2FNN-A). The RT2FNN-A uses the interval asymmetric type-2 fuzzy sets and it implements the FLS in a five layer neural network structure which contains four layer forward network and a feedback layer. Each asymmetric fuzzy member function (AFMF) is constructed by parts of four Gaussian functions. The corresponding learning algorithm is derived by gradient descent method. Finally, the RT2FNN-A is applied in identification of nonlinear dynamic system. Simulation results are shown to illustrate the effectiveness of the RT2FNN-A systems.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
Pages1496-1502
Number of pages7
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008 - Hong Kong, China
Duration: 1 Jun 20086 Jun 2008

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
CountryChina
CityHong Kong
Period1/06/086/06/08

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