A Functional-Link based Interval Type-2 Compensatory Fuzzy Neural Network for Nonlinear System modeling

Jyh-Yeong Chang, Yang-Yin Lin, Ming-Feng Han, Chin-Teng Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

In this paper, the Functional-Link based Interval Type-2 Compensatory Fuzzy Neural Network (FLIT2CFNN) is a six-layer structure, which combines compensatory fuzzy reasoning method, and the consequent part is combined the proposed functional-link neural network with interval weights. The compensatory fuzzy reasoning method uses adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic system more adaptive and effective. Initially, there is no rule in the FLIT2CFNN. A FLIT2CFNN is constructed using concurrent structure and parameter learning. The advantages of this learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. All of the antecedent part parameters and compensatory degree values are learned by gradient descent algorithm. Several simulation results show that the FLIT2CFNN achieves better performance than other feedforword type-1 and type-2 FNNs.
Original languageEnglish
Title of host publicationIEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)
PublisherIEEE
Pages 939-943
Number of pages5
ISBN (Print)978-1-4244-7317-5
DOIs
StatePublished - 2010
Event2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan
Duration: 27 Jun 201130 Jun 2011

Publication series

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

Conference

Conference2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
CountryTaiwan
CityTaipei
Period27/06/1130/06/11

Keywords

  • type-2 fuzzy systems; compensatory operation; structure learning; on-line fuzzy clustering

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    Chang, J-Y., Lin, Y-Y., Han, M-F., & Lin, C-T. (2010). A Functional-Link based Interval Type-2 Compensatory Fuzzy Neural Network for Nonlinear System modeling. In IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011) (pp. 939-943). (IEEE International Conference on Fuzzy Systems). IEEE. https://doi.org/10.1109/FUZZY.2011.6007477