A recurrent functional-link-based neural fuzzy system and its applications

Cheng-Hung Chen, Cheng-Jian Lin, Chin-Teng Lin

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

5 Scopus citations

Abstract

In this paper, a recurrent functional-link-based neural fuzzy system (RFLNFS) is proposed for prediction of time sequence and skin color detection. The proposed RFLNFS model uses functional link neural network as the consequent part of fuzzy rules. The RFLNFS model can generate the consequent part of a nonlinear combination of the input variables. The recurrent network is embedded in the RFLNFS by adding feedback connections in the second layer, where the feedback units act as memory elements. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. Finally, the RFLNFS is applied to two simulations. The simulation results of the dynamic system modeling have shown that the RFLNFS model can solve the temporal problem and the RFLNFS model has superior performance than other models.
Original languageEnglish
Title of host publicationIEEE Symposium on Computational Intelligence in Image and Signal Processing
PublisherIEEE
Pages415
ISBN (Print)978-1-4244-0707-1
DOIs
StatePublished - 2007
Event2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007 - Honolulu, HI, United States
Duration: 1 Apr 20075 Apr 2007

Publication series

Name2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN IMAGE AND SIGNAL PROCESSING
PublisherIEEE

Conference

Conference2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007
CountryUnited States
CityHonolulu, HI
Period1/04/075/04/07

Keywords

  • NETWORKS; IDENTIFICATION

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  • Cite this

    Chen, C-H., Lin, C-J., & Lin, C-T. (2007). A recurrent functional-link-based neural fuzzy system and its applications. In IEEE Symposium on Computational Intelligence in Image and Signal Processing (pp. 415). (2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN IMAGE AND SIGNAL PROCESSING). IEEE. https://doi.org/10.1109/CIISP.2007.369205