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Related Experiment Video

Updated: Apr 30, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Multikernel least mean square algorithm.

Felipe A Tobar, Sun-Yuan Kung, Danilo P Mandic

IEEE Transactions on Neural Networks and Learning Systems

|May 9, 2014

Summary

This summary is machine-generated.

A new multikernel least-mean-square algorithm enables adaptive estimation of complex nonlinear and nonstationary signals. This method efficiently handles multivariate data in dynamic environments, improving adaptive prediction accuracy.

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Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

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Last Updated: Apr 30, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

Signal Processing
Machine Learning
Adaptive Systems

Background:

Adaptive estimation of nonlinear and nonstationary signals is challenging.
Existing methods may struggle with high-dimensional, time-varying data.
Efficient algorithms are needed for real-world dynamic systems.

Purpose of the Study:

Introduce a novel multikernel least-mean-square algorithm.
Enable adaptive estimation for vector-valued nonlinear and nonstationary signals.
Provide a computationally efficient solution for dynamic environments.

Main Methods:

Mapping multivariate data to a time-varying vector-valued function Hilbert space.
Utilizing adaptive sparsification criteria for a finite dictionary.
Employing multikernel least-squares within a vector-valued reproducing kernel Hilbert space.

Main Results:

The proposed algorithm demonstrates computational efficiency.
It is suitable for nonstationary environments and adaptive prediction.
Simulations confirm effectiveness on body sensor and wind signals.

Conclusions:

The multikernel least-mean-square algorithm offers robust adaptive estimation.
Vector-valued reproducing kernel Hilbert spaces are effective feature spaces.
The approach is validated for nonlinear and nonstationary signal processing.