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19th IFAC Symposium on System Identification (SYSID)

作者： Barbieri, Matteo Diversi, Roberto Univ Bologna Dept Elect Elect & Informat Engn Viale Risorgimento 2 I-40136 Bologna Italy

The identification of errors-in-variables (EIV) models refers to systems where the available measurements of their inputs and outputs are corrupted by additive noise. A large variety of solutions are available when dealing with this estimation problem, in particular when the corrupting noises are white processes. However, the number of available solutions decreases when the output noise is assumed as a colored process, which is a case of great practical interest. On the other hand, many applications require estimation algorithms to work on-line, tracking a dynamical system behavior for control, signal processing, or diagnosis. In many cases, they even have to take into account computational constraints. In this paper, we propose an estimation method that is able to both lay out an algorithm to solve the identification problem of EIV systems with arbitrarily correlated output noise and also provide an efficient recursive version that does not make use of variable size matrix inversions. Copyright (C) 2021 The Authors.

关键词： System Identification errors-in-variables models Recursive Identification Correlated output noise Compensated Normal Equations High-Order Yule-Walker Equations

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Recursive identification of errors-in-variables models with correlated output noise

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IFAC-PapersOnLine 2021年第7期54卷 363-368页

作者： Matteo Barbieri Roberto Diversi Department of Electrical Electronic and Information Engineering University of Bologna Viale del Risorgimento 2 40136 Bologna Italy

关键词： System Identification errors-in-variables models Recursive Identification Correlated output noise Compensated Normal Equations High-Order Yule-Walker Equations

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An improved computationally efficient identification method for errors-in-variables models based on v-gap optimisation

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INTERNATIONAL JOURNAL OF CONTROL 2019年第9期92卷 2150-2158页

作者： Geng, Li-Hui Tianjin Univ Technol & Educ Sch Automat & Elect Engn Tianjin Key Lab Informat Sensing & Intelligent Co Tianjin Peoples R China

A v-gap optimisation-based frequency-domain identification method is proposed to estimate a nominal normalised right graph symbol for errors-in-variables models. The proposed method follows the similar identification idea from a previous research work but with improved computational efficiency using interior-point (IP) algorithms. By imposing an inner function constraint instead of frequency point-wise constraints to normalise the graph symbol, the number of involved equality constraints for the v-gap optimisation is related to the nominal model order instead of the data length. As a consequence, the computational complexity of the proposed IP-based identification algorithm is much lower than that of linear matrix inequalities-based algorithms. Due to the fact that the data length is typically much larger than the finite nominal model order, the number of saved equality constraints is close to that of the employed data points. Finally, two numerical simulation examples are given to verify the proposed identification method.

关键词： V-gap optimisation computational efficiency errors-in-variables models interior-point algorithm

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Constrained Cramer-Rao Lower Bound in errors-In variables (EIV) models: Revisited

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STATISTICS & PROBABILITY LETTERS 2018年 135卷 118-126页

作者： Al-Sharadqah, A. Ho, K. C. Calif State Univ Northridge Dept Math Northridge CA 91330 USA Calif State Univ Northridge IRIS Northridge CA 91330 USA Univ Missouri Dept Elect & Comp Engn Columbia MO 65211 USA

The Constrained Cramer-Rao Lower Bound (CCRB) works only for an unbiased estimator. The CCRB of Stoica and Ng (1998) is revisited and generalized. The bound is applied to two applications in the nonlinear EIV models. ... 详细信息

关键词： Constrained Cramer Rao Lower Bound errors-in-variables models Functional model

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SMOOTH BACKFITTING FOR errors-IN-variables ADDITIVE models

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ANNALS OF STATISTICS 2018年第5期46卷 2216-2250页

作者： Han, Kyunghee Park, Byeong U. Seoul Natl Univ Dept Stat 1 Gwanak Ro Seoul 08826 South Korea

In this work, we develop a new smooth backfitting method and theory for estimating additive nonparametric regression models when the covariates are contaminated by measurement errors. For this, we devise a new kernel function that suitably deconvolutes the bias due to measurement errors as well as renders a projection interpretation to the resulting estimator in the space of additive functions. The deconvolution property and the projection interpretation are essential for a successful solution of the problem. We prove that the method based on the new kernel weighting scheme achieves the optimal rate of convergence in one-dimensional deconvolution problems when the smoothness of measurement error distribution is less than a threshold value. We find that the speed of convergence is slower than the univariate rate when the smoothness of measurement error distribution is above the threshold, but it is still much faster than the optimal rate in multivariate deconvolution problems. The theory requires a deliberate analysis of the nonnegligible effects of measurement errors being propagated to other additive components through backfitting operation. We present the finite sample performance of the deconvolution smooth backfitting estimators that confirms our theoretical findings.

关键词： Nonparametric additive regression smooth backfitting errors-in-variables models deconvolution kernel smoothing

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Bias-eliminating least-squares identification of errors-in-variables models with mutually correlated noises

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2013年第10期27卷 915-924页

作者： Diversi, Roberto Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marcon I-40136 Bologna Italy

This paper proposes a bias-eliminating least-squares (BELS) approach for identifying linear dynamic errors-in-variables (EIV) models whose input and output are corrupted by additive white noise. The method is based on an iterative procedure involving, at each step, the estimation of both the system parameters and the noise variances. The proposed identification algorithm differs from previous BELS algorithms in two aspects. First, the input and output noises are allowed to be mutually correlated, and second, the estimation of the noise covariances is obtained by exploiting the statistical properties of the equation error of the EIV model. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： system identification errors-in-variables models bias-eliminating least squares mutually correlated white noises

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Diffusion Complex Total Least Mean M-Estimate Adaptive Algorithm for Distributed Networks and Impulsive Noise

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第9期71卷 4391-4395页

作者： Zhao, Haiquan Cao, Zian Chen, Yida Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

Complex domain adaptive filtering algorithms (CD-AFA) have attracted the attention of researchers. However, there are very few studies on AFA for complex-valued errors-in-variables (CEIV) models. Additionally, considering the advantages of distributed methods in various environments such as wireless and sensor networks, this brief proposes the diffusion complex total least mean M-Estimate adaptive filtering algorithm (DCTLMM-AFA). This algorithm extends the total least squares (TLS) method to complex domain and applies it to distributed networks for the first time. In addition, the DCTLMM-AFA also robust to impulsive noise is attributed to the characteristics of the M estimation function (MF). Finally, this brief provides an exhaustive analysis of the algorithm's theoretical performance in the presence of impulsive noise. Simulation results substantiate both the advantages of the proposed algorithm and the accuracy of the theoretical analysis presented in this brief.

关键词： Convergence Circuits Frequency estimation Estimation Steady-state Power systems Noise Total least squares mean M-estimate diffusion complex-valued errors-in-variables models adaptive filter

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Complete convergence and complete moment convergence for weighted sums of random variables satisfying generalized Rosenthal type inequalities and an application

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STOCHASTICS-AN INTERNATIONAL JOURNAL OF PROBABILITY AND STOCHASTIC PROCESSES 2024年第5期96卷 1475-1506页

作者： Zheng, Xiaoqian Wang, Chunhua Wang, Xuejun Anhui Univ Sch Big Data & Stat Hefei 230601 Peoples R China Anhui Univ Chinese Med Sch Informat Engn Hefei Peoples R China

This article mainly investigates the complete convergence and complete moment convergence for weighted sums of random variables satisfying generalized Rosenthal type inequalities, which improves the corresponding ones of the literature. As an application, we further research the errors-in-variables (EV, for short) regression models and exhibit the strong consistency for the least square (LS, for short) estimators. In addition, simulation studies are conducted to verify the finite sample performance of theoretical results.

关键词： Complete convergence complete moment convergence Rosenthal type inequalities errors-in-variables models strong consistency

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Complete f-moment convergence for m-asymptotic negatively associated random variables and related statistical applications

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JOURNAL OF NONPARAMETRIC STATISTICS 2024年第4期36卷 911-939页

作者： Wang, Xuejun Chen, Xi Hu, Tien-Chung Volodin, Andrei Anhui Univ Sch Big Data & Stat Hefei 230601 Peoples R China Natl Tsing Hua Univ Dept Math Hsinchu Taiwan Xiamen Univ Technol Sino Canada Res Ctr Nonlinear Dynam & Noise Contro Xiamen Fujian Peoples R China Univ Regina Regina SK Canada Xiamen Univ Technol Xiamen Fujian Peoples R China Univ Regina Dept Math & Stat Regina SK Canada

In this article, the complete f-moment convergence for m-asymptotic negatively associated random variables is investigated. As applications, we establish the strong consistency of the least square estimator in the simple linear errors-in-variables models and the complete consistency for estimator in the semiparametric regression model based on m-asymptotic negatively associated errors. We also give some simulations to assess the finite sample performance of the theoretical results.

关键词： m-Asymptotic negatively associated random variables complete f-moment convergence consistency errors-in-variables models semiparametric regression models

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Structured errors-in-variables Modelling for Cortico-Muscular Coherence Enhancement 48

Structured Errors-in-Variables Modelling for Cortico-Muscula...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Guo, Zhenghao McClelland, Verity M. Dai, Wei Cvetkovic, Zoran King's College Department of Engineering London United Kingdom King's College Department of Basic and Clinical Neuroscience London United Kingdom Imperial College Department of Electrical and Electronic Engineering London United Kingdom

ISBN: (纸本)9781728163277

Functional coupling between the cortex and muscle is commonly quantified by cortico-muscular coherence (CMC) between electroencephalogram (EEG) and electromyogram (EMG) signals. However, the presence of noise in EEG and EMG often degrades CMC, making it challenging to detect: some healthy subjects with good motor skills show no significant CMC. This study proposes an approach based on structured errors-in-variables (EIV) modelling to estimate components of the cortex and muscle signals involved in movement control from noisy EEG and EMG signals for the purpose of coherence estimation. We describe three algorithms to identify the underlying EIV system: one based on total least squares;the other two on structured total least squares, in which the Toeplitz data matrix structure is preserved. The effectiveness of the proposed method is assessed using simulated and neurophysiological data, where it achieved considerable improvements in coherence levels. © 2023 IEEE.

关键词： Cortico-muscular coherence EEG EMG errors-in-variables models structured total least squares

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