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Enhanced FIR system identification: Empirical copula delay estimation method and variable stacking length multi-gradient algorithm

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  • Jing, Shaoxue

Abstract

This study offers an improved strategy for identifying finite impulse response (FIR) systems. To begin with, a modified time delay estimation method, rooted in empirical copula, is applied, creating a robust framework for parameter estimation. Following this, an innovative stochastic gradient algorithm that incorporates a flexible stacking length is presented to enhance the accuracy of parameter estimates. This algorithm adapts stacking length dynamically according to the optimization progress, which enhances both convergence and accuracy. Additionally, a criterion based on the decrease of the cost function is proposed to determine the optimal variable stacking length, ensuring better performance. Extensive experimental results confirm the effectiveness of the proposed methods.

Suggested Citation

  • Jing, Shaoxue, 2026. "Enhanced FIR system identification: Empirical copula delay estimation method and variable stacking length multi-gradient algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 241(PA), pages 676-688.
  • Handle: RePEc:eee:matcom:v:241:y:2026:i:pa:p:676-688
    DOI: 10.1016/j.matcom.2025.09.029
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    References listed on IDEAS

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    1. Abubakar, Auwal Bala & Kumam, Poom & Ibrahim, Abdulkarim Hassan & Chaipunya, Parin & Rano, Sadiya Ali, 2022. "New hybrid three-term spectral-conjugate gradient method for finding solutions of nonlinear monotone operator equations with applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 670-683.
    2. Berghaus, Betina & Segers, Johan, 2017. "Weak convergence of the weighted empirical beta copula process," LIDAM Discussion Papers ISBA 2017015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Segers, Johan & Sibuya, Masaaki & Tsukahara, Hideatsu, 2017. "The empirical beta copula," LIDAM Reprints ISBA 2017005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Segers, Johan & Sibuya, Masaaki & Tsukahara, Hideatsu, 2017. "The empirical beta copula," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 35-51.
    5. M. Belalia & T. Bouezmarni & F. C. Lemyre & A. Taamouti, 2017. "Testing independence based on Bernstein empirical copula and copula density," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(2), pages 346-380, April.
    6. Abubakar, Auwal Bala & Kumam, Poom & Malik, Maulana & Ibrahim, Abdulkarim Hassan, 2022. "A hybrid conjugate gradient based approach for solving unconstrained optimization and motion control problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 640-657.
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