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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Computationally efficient robust adaptive filtering algorithm based on improved minimum error entropy criterion with

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  • 1Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, China.

ISA Transactions
|April 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an improved minimum error entropy criterion (IMEEF) to enhance adaptive filtering. The new algorithm (IMEEF-GD) reduces computational complexity while maintaining performance in noise.

Keywords:
Acoustic echo cancellationAdaptive filteringComputational complexityMinimum error entropy criterion with fiducial pointsPerformance analysis

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Area of Science:

  • Information Theoretic Learning
  • Adaptive Filtering
  • Signal Processing

Background:

  • Minimum Error Entropy (MEE) is effective for multimodal non-Gaussian noise but is shift-invariant.
  • MEE's insensitivity to error location is a limitation.
  • Combining MEE with Maximum Correntropy (MC) yields MEEF, but with high computational cost.

Purpose of the Study:

  • To develop a computationally efficient adaptive filtering algorithm.
  • To improve upon the MEEF criterion by reducing complexity.
  • To analyze the convergence and performance of the proposed algorithm.

Main Methods:

  • Devised an improved MEEF (IMEEF) criterion to avoid redundant calculations.
  • Proposed a gradient descent (GD) based adaptive filtering algorithm (IMEEF-GD).
  • Analyzed convergence conditions (mean sense) and steady-state/transient behaviors (mean-square sense).

Main Results:

  • The IMEEF-GD algorithm shows computational requirements independent of error sample number.
  • Theoretical models align well with observed learning curves.
  • The algorithm demonstrates effectiveness in system identification, prediction, and acoustic echo cancellation.

Conclusions:

  • The proposed IMEEF-GD algorithm offers an efficient solution for adaptive filtering.
  • It effectively addresses the limitations of previous MEE-based methods.
  • Validated through diverse applications like system identification and acoustic echo cancellation.