Adaptive Fuzzy Filtering in a Deterministic Setting
- 30 April 2008
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Fuzzy Systems
- Vol. 17 (4), 763-776
- https://doi.org/10.1109/tfuzz.2008.924331
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).Keywords
This publication has 34 references indexed in Scilit:
- Handling uncertainties in toxicity modelling using a fuzzy filterSAR and QSAR in Environmental Research, 2007
- Robust Solution to Fuzzy Identification Problem with Uncertain Data by RegularizationFuzzy Optimization and Decision Making, 2004
- Regularized Adaptation of Fuzzy Inference Systems. Modelling the Opinion of a Medical Expert about Physical Fitness: An ApplicationFuzzy Optimization and Decision Making, 2003
- The Robustness of the p-Norm AlgorithmsMachine Learning, 2003
- Robust Adaptive Fuzzy Identification of Time-Varying Processes with Uncertain Data. Handling Uncertainties in the Physical Fitness Fuzzy Approximation with Real World Medical Data: An ApplicationFuzzy Optimization and Decision Making, 2003
- Adaptive and Self-Confident On-Line Learning AlgorithmsJournal of Computer and System Sciences, 2002
- Regularized data-driven construction of fuzzy controllersJIIP, 2002
- Relative loss bounds for single neuronsIEEE Transactions on Neural Networks, 1999
- Worst-case quadratic loss bounds for prediction using linear functions and gradient descentIEEE Transactions on Neural Networks, 1996
- Solving Least Squares ProblemsPublished by Society for Industrial & Applied Mathematics (SIAM) ,1995