Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Fuzzy identification from a grey box modeling point of view
Fuzzy model identification
Relative Loss Bounds for Multidimensional Regression Problems
Machine Learning
Adaptive filters with error nonlinearities: mean-square analysis and optimum design
EURASIP Journal on Applied Signal Processing - Nonlinear signal and image processing - part I
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
The Robustness of the p-Norm Algorithms
Machine Learning
Robust Solution to Fuzzy Identification Problem with Uncertain Data by Regularization
Fuzzy Optimization and Decision Making
Information geometry of U-Boost and Bregman divergence
Neural Computation
Robust Adaptive Identification of Fuzzy Systems with Uncertain Data
Fuzzy Optimization and Decision Making
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Structured Prediction, Dual Extragradient and Bregman Projections
The Journal of Machine Learning Research
A unified approach to the steady-state and tracking analyses ofadaptive filters
IEEE Transactions on Signal Processing
H∞ optimality of the LMS algorithm
IEEE Transactions on Signal Processing
The p-norm generalization of the LMS algorithm for adaptive filtering
IEEE Transactions on Signal Processing
Transient analysis of adaptive filters with error nonlinearities
IEEE Transactions on Signal Processing
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Deterministic approach to robust adaptive learning of fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Noisy speech processing by recurrently adaptive fuzzy filters
IEEE Transactions on Fuzzy Systems
Fuzzy identification using fuzzy neural networks with stable learning algorithms
IEEE Transactions on Fuzzy Systems
Adaptive noise cancellation using enhanced dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
A robust design criterion for interpretable fuzzy models with uncertain data
IEEE Transactions on Fuzzy Systems
Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment
IEEE Transactions on Fuzzy Systems
An energy-gain bounding approach to robust fuzzy identification
Automatica (Journal of IFAC)
On the optimality of conditional expectation as a Bregman predictor
IEEE Transactions on Information Theory
Worst-case quadratic loss bounds for prediction using linear functions and gradient descent
IEEE Transactions on Neural Networks
Relative loss bounds for single neurons
IEEE Transactions on Neural Networks
On the estimation of parameters of Takagi-Sugeno fuzzy filte
IEEE Transactions on Fuzzy Systems
Variational bayes for a mixed stochastic/deterministic fuzzy filter
IEEE Transactions on Fuzzy Systems
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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).