A TSK-Type Fuzzy Neural Network (TFNN) Systems for Dynamic Systems Identification

Ching Hung Lee*, Wei Yu Lai, Yu Ching Lin

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

31 Scopus citations

Abstract

In this paper, a TSK-type fuzzy neural network system (TFNN) for identifying unknown dynamic systems is proposed. The TFNN system can learn its knowledge base from input-output training data. Thus, the unknown system is represented as several if-then rules with TSK-type consequent parts. The TFNN system can be randomly initialized and then trained by the back-propagation algorithm. Several examples are presented to illustrate the effectiveness of our approach. fuzzy neural network, TSK-type fuzzy systems, back-propagation algorithm, system identification.

Original languageEnglish
Pages (from-to)4002-4007
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume4
DOIs
StatePublished - 2003
Event42nd IEEE Conference on Decision and Control - Maui, HI, United States
Duration: 9 Dec 200312 Dec 2003

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