Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (12): 3673-3680.doi: 10.13229/j.cnki.jdxbgxb.20240613

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Repetitive gradient learning parameter estimation of quantized Wiener system

Hao-zhe CAO(),Jin-ben ZHOU,Li-hua LI()   

  1. School of National Security，People’s Public Security University of China，Beijing 100038，China
  • Received:2024-06-03 Online:2024-12-01 Published:2025-01-24
  • Contact: Li-hua LI E-mail:caohaozhe@ppsuc.edu.cn;lilihua@ppsuc.edu.cn

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Abstract:

In order to address the identification of the quantized Wiener system， a repetitive gradient learning identification algorithm was proposed. Firstly， based on the decomposition technique， the quantified Wiener system was transformed into an identification model with parameter separation， in which the computational burden of was reduced. Secondly， the observation data were extended using data window theory to obtain more quantitative system modal information. To address the issue of moving time window length， the idea of repetitive learning was integrated into the parameter adaptive law update mechanism， which greatly improves the estimation performance. Finally， the convergence of the estimator and the comparisons of example have used to show the effectiveness and advantages of the proposed algorithm.

Key words: systems engineering, system identification, Wiener system, gradient estimation, repetitive learning, quantized observation

CLC Number: 

  • TP273

Fig.1

Schematic diagram of diagram of quantized system"

Fig.2

Parameter estimation curve of g2"

Fig.3

Parameter estimation curve of g3"

Fig.4

Parameter estimation curve of a1"

Fig.5

Parameter estimation curve of a2"

Fig.6

Parameter estimation curve of b1"

Fig.7

Parameter estimation curve of b2"

Fig.8

Model validation"

Fig.9

Estimation errors of MDWRLG under different window lengths"

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