NDLI: On estimation of temporal fuzzy sets for signal analysis: FCM vs. FMLE approaches

Content Provider	IEEE Xplore Digital Library
Author	Kosanovic, B.R. Chaparro, L.F. Sclabassi, R.J.
Copyright Year	1995
Description	Author affiliation: Lab. for Comput. Neurosci., Pittsburgh Univ., PA, USA (Kosanovic, B.R.)
Abstract	Estimation of temporal fuzzy sets that model dynamic processes is discussed. It has been found that although poles of attraction can be estimated fairly well with different fuzzy partitioning algorithms, membership function estimates may fail in accurately describing dynamic changes within the observed signals. Two types of fuzzy partitioning algorithms are compared: fuzzy c-means (FCM) and fuzzy maximum likelihood (FMLE). The simulations performed on quasi stationary Gaussian signals suggest that the membership functions estimated by FMLE fail to follow continuous changes of dynamics, while those estimated by FCM provide a good compromise between precision and physical relevance.
Starting Page	583
Ending Page	588
File Size	513905
Page Count	6
File Format	PDF
ISBN	0818671262
DOI	10.1109/ISUMA.1995.527760
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-09-17
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Signal analysis Clustering algorithms Prototypes Partitioning algorithms Signal processing Hidden Markov models Surgery Maximum likelihood estimation Motion measurement
Content Type	Text
Resource Type	Article

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On estimation of temporal fuzzy sets for signal analysis: FCM vs. FMLE approaches