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Your search keyword '"Zhao, Haiquan"' showing total 2 results
Start Over Author "Zhao, Haiquan" Topic cost functions Topic parameter estimation Publication Year Range Last 3 years
2 results on '"Zhao, Haiquan"'

1. Robust Adaptive Least Mean M-Estimate Algorithm for Censored Regression.

Author

Wang, Gen and Zhao, Haiquan

Subjects
*PARAMETER estimation, *SYSTEM identification, *COST functions, *REGRESSION analysis, *COMPUTER systems, *ALGORITHMS, *PROBIT analysis
Abstract

An adaptive least mean M-estimate algorithm for censored regression (CR-LMM) is presented for the robust parameter estimation of the censored regression system. To correct the bias produced by censored observation, the estimated error derived from the probit regression model is employed to construct an M-estimate cost function. It can expel the adverse impact of the impulsive noise and is solved by the unconstrained optimization method. Furthermore, the robust variable step-size (VSS) strategy, which is also predicted on the robust cost function, is also utilized to improve the convergence performance of the proposed CR-LMM algorithm, i.e., convergence speed and steady-state mean square deviation. The condition which guarantees the CR-LMM algorithm stability is obtained by analyzing the convergence in the mean and mean-square sense, and the theoretical steady-state result is also derived. Computer simulations in system identification scenarios are carried out to demonstrate that the proposed algorithms are superior to the existing algorithms in the impulsive environment with different background noise and the theoretical results are verified. [ABSTRACT FROM AUTHOR]

Published
2022
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2. A class of robust censored regression adaptive filtering algorithms.

Author

Liu, Dongxu, Zhao, Haiquan, and Zhou, Yang

Subjects
*ADAPTIVE filters, *PARAMETER estimation, *COST functions, *FILTERS & filtration, *STOCHASTIC analysis, *BEHAVIORAL assessment
Abstract

To improve the robust characteristic and filtering accuracy of the traditional adaptive filtering algorithms (AFAs) under censored regression (CR) model in impulsive interference environments, some robust CR-type AFAs (R-CR-AFAs) have been developed. However, these exiting R-CR-AFAs may suffer from high steady-state misalignment in parameter estimation scenario. Therefore, in this paper, we first construct a unified framework to develop a class of R-CR-AFAs by adopting various robust strategies with error nonlinearities as cost functions. Then the mean and mean-square stability ranges and steady-state convergence behavior of the R-CR-AFAs are analyzed via resorting to some frequently-utilized assumptions and lemmas. In particular, a novel censored regression generalized modified Blake-Zisserman adaptive filtering (CR-GMBZ-AF) algorithm is presented to achieve higher steady-state estimation accuracy contrasted to related R-CR-AFAs benefiting from the advantages of the newly proposed GMBZ criterion based on the unified framework. Numerical simulation experiments manifest that the developed CR-GMBZ-AF algorithm arrives at better learning performance in comparison with state-of-the-art algorithms and the theoretical steady-state model demonstrates good agreement with simulation results under parameter estimation and acoustic echo cancellation scenarios. [ABSTRACT FROM AUTHOR]

Published
2024
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