NDLI: Adaptive Fuzzy Filtering in a Deterministic Setting

Content Provider	IEEE Xplore Digital Library
Author	Kumar, M. Stoll, N. Stoll, R.
Copyright Year	1993
Abstract	Many real-world applications involve the filtering and estimation of process variables. This study considers the use of interpretable Sugeno-type fuzzy models for adaptive filtering. Our aim in this study is to provide different adaptive fuzzy filtering algorithms in a deterministic setting. The algorithms are derived and studied in a unified way without making any assumptions on the nature of signals (i.e., process variables). The study extends, in a common framework, the adaptive filtering algorithms (usually studied in signal processing literature) and p -norm algorithms (usually studied in machine learning literature) to semilinear fuzzy models. A mathematical framework is provided that allows the development and an analysis of the adaptive fuzzy filtering algorithms. We study a class of nonlinear LMS-like algorithms for the online estimation of fuzzy model parameters. A generalization of the algorithms to the p-norm is provided using Bregman divergences (a standard tool for online machine learning algorithms).
Sponsorship	IEEE Computational Intelligence Society
Starting Page	763
Ending Page	776
Page Count	14
File Size	815883
File Format	PDF
ISSN	10636706
Volume Number	17
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Adaptive filters Filtering algorithms Signal processing algorithms Machine learning algorithms Uncertainty Robustness Automation Least squares approximation Signal processing Sugeno fuzzy models Adaptive filtering algorithms Bregman divergences $p$-norm robustness adaptive filtering algorithms p-norm
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Control and Systems Engineering Computational Theory and Mathematics

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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