Fuzzy modeling based on generalized neural networks and fuzzy clustering objective functions

Chuen-Tsai Sun*, Jyh Shing Jang

*Corresponding author for this work

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

6 Scopus citations

Abstract

An approach to the formulation of fuzzy if-then rules based on clustering objective functions is proposed. The membership functions are then calibrated with the generalized neural networks technique to achieve a desired input-output mapping. The learning procedure is basically a gradient-descent algorithm. A Kalman filter algorithm is used to improve the overall performance.

Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Decision and Control
PublisherPubl by IEEE
Pages2924-2929
Number of pages6
ISBN (Print)0780304500
DOIs
StatePublished - 1 Dec 1991
EventProceedings of the 30th IEEE Conference on Decision and Control Part 1 (of 3) - Brighton, Engl
Duration: 11 Dec 199113 Dec 1991

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume3
ISSN (Print)0191-2216

Conference

ConferenceProceedings of the 30th IEEE Conference on Decision and Control Part 1 (of 3)
CityBrighton, Engl
Period11/12/9113/12/91

Fingerprint Dive into the research topics of 'Fuzzy modeling based on generalized neural networks and fuzzy clustering objective functions'. Together they form a unique fingerprint.

  • Cite this

    Sun, C-T., & Jang, J. S. (1991). Fuzzy modeling based on generalized neural networks and fuzzy clustering objective functions. In Proceedings of the IEEE Conference on Decision and Control (pp. 2924-2929). (Proceedings of the IEEE Conference on Decision and Control; Vol. 3). Publ by IEEE. https://doi.org/10.1109/CDC.1991.261075