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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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Convergence and performance analysis of kernel regularized robust recursive least squares.

Alireza Naeimi Sadigh1, Hadi Sadoghi Yazdi1, Ahad Harati1

  • 1Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

ISA Transactions
|May 24, 2020
PubMed
Summary
This summary is machine-generated.

We introduce Kernel Regularized Robust RLS (KR3LS), a novel filter robust to non-Gaussian noise. This method offers proven convergence and superior performance over existing robust alternatives.

Keywords:
Half-quadratic optimizationKernel robust recursive least squaresNon-Gaussian noisePerformance analysis

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Robust Statistics

Background:

  • Kernel Recursive Least Squares (KRLS) algorithms are sensitive to non-Gaussian noise.
  • Existing robust extensions lack theoretical convergence analysis due to model complexity.

Purpose of the Study:

  • To propose a new robust filter, Kernel Regularized Robust RLS (KR3LS).
  • To provide theoretical convergence analysis for the proposed filter.
  • To demonstrate the superiority of KR3LS over existing methods.

Main Methods:

  • Utilizing the half-quadratic technique to simplify the loss function.
  • Developing a novel Kernel Regularized Robust RLS (KR3LS) algorithm.
  • Conducting theoretical analysis to prove filter convergence and derive regularization factor bounds.

Main Results:

  • The convergence of KR3LS to target weights and desired output is theoretically proven.
  • Bounds for the regularization factor were successfully obtained.
  • Experimental results on synthetic and real data show KR3LS outperforms other robust alternatives.

Conclusions:

  • KR3LS offers a theoretically sound and robust alternative for signal processing in noisy environments.
  • The proposed method provides superior performance compared to existing robust filters.
  • The theoretical analysis validates the stability and effectiveness of KR3LS.