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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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[Formula: see text]-regularized recursive total least squares based sparse system identification for the

Jun-Seok Lim1, Hee-Suk Pang1

  • 1Department of Electronics Engineering, Sejong University, Kunja, Kwangjin, 98, 143-747 Seoul, Korea.

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|September 22, 2016
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Summary
This summary is machine-generated.

This study introduces a novel L0-regularized recursive total least squares (RTLS) algorithm to address noise in sparse system identification. The new method improves estimation accuracy in error-in-variables scenarios.

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Adaptive filterConvex regularizationRLSSparsityTLS[Formula: see text]1-norm

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Area of Science:

  • Signal Processing
  • System Identification
  • Machine Learning

Background:

  • Recursive Least Squares (RLS) is used for sparse system identification.
  • RLS performance degrades when both input and output data contain noise (error-in-variables problem).

Purpose of the Study:

  • To propose an algorithm that effectively handles the error-in-variables problem in sparse system identification.
  • To improve estimation performance compared to existing RLS-based methods.

Main Methods:

  • An L0-regularized recursive total least squares (L0-RTLS) algorithm is developed.
  • The algorithm employs an L0 regularization technique within an RLS-like iterative framework.
  • Efficient handling of inversion matrices is incorporated to reduce computational complexity.

Main Results:

  • The proposed L0-RTLS algorithm demonstrates excellent performance in sparse system identification.
  • The algorithm effectively mitigates the negative impact of noise in both input and output data.
  • Simulations confirm the superiority of the L0-RTLS approach over existing methods.

Conclusions:

  • The L0-regularized RTLS algorithm provides a robust solution for sparse system identification with noisy data.
  • The method offers improved accuracy and reduced complexity, making it suitable for practical applications.