A Class of Diffusion Proportionate Subband Adaptive Filters for Sparse System Identification over Distributed Networks

Pouradabi, Ahmad; Rastegarnia, Amir; Zandi, Sajad; Bazzi, Wael M.; Sanei, Saeid

doi:10.1007/s00034-021-01766-x

Cited by 4 publications

(1 citation statement)

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“…Noise coexists with system data during the process of collecting identification data. Several filters for reducing noise signals have been proposed 23 – 27 . A linear filter was used to obtain filtered input and output information, and an overparameterization scheme was proposed to recover parameter information in 28 .…”

Section: Introductionmentioning

confidence: 99%

Adaptive parameter estimation for the expanded sandwich model

Yang

Zhang

Liu

et al. 2023

Sci Rep

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An expanded-sandwich system is a nonlinear extended block-oriented system in which memoryless elements in conventional block-oriented systems are displaced by memory submodels. Expanded-sandwich system identification has received extensive attention in recent years due to the powerful ability of these systems to describe actual industrial systems. This study proposes a novel recursive identification algorithm for an expanded-sandwich system, in which an estimator is developed on the basis of parameter identification error data rather than the traditional prediction error output information. In this scheme, a filter is introduced to extract the available system information based on miserly structure layout, and some intermediate variables are designed using filtered vectors. According to the developed intermediate variables, the parameter identification error data can be obtained. Thereafter, an adaptive estimator is established by integrating the identification error data compared with the classic adaptive estimator based on the prediction error output information. Thus, the design framework introduced in this research provides a new perspective for the design of identification algorithms. Under a general continuous excitation condition, the parameter estimation values can converge to the true values. Finally, experimental results and illustrative examples indicate the availability and usefulness of the proposed method.

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Section: Introductionmentioning

confidence: 99%