Frequency domain identification of FIR models in the presence of additive input–output noise

Soverini, Umberto; Söderström, Torsten

doi:10.1016/j.automatica.2020.108879

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…In this section, we present three numerical examples, similar to [14], [62], to analyze the performance of our proposal. This framework of numerical simulations is typically used when the performance of new estimation algorithms are tested with problems for which an experimental setup cannot be planned or to reduce the costs of experiments [63], [64].…”

Section: Numerical Examplesmentioning

confidence: 99%

“…A number of techniques have been developed to deal with EIV system identification, such as total least squares, instrumental variables, bias-compensation, covariance match-ing, high-order statistic (HOS), maximum likelihood (ML), among others (see e.g. [1], [2], [12]- [14]). Some assumptions can be used to achieve the EIV system identifiability.…”

Section: Introductionmentioning

confidence: 99%

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Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models

et al. 2023

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In this paper a Maximum likelihood estimation algorithm for Finite Impulse Response Errors-in-Variables systems is developed. We consider that the noise-free input signal is Gaussian-mixture distributed. We propose an Expectation-Maximization-based algorithm to estimate the system model parameters, the input and output noise variances, and the Gaussian mixture noise-free input parameters. The benefits of our proposal are illustrated via numerical simulations.

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Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models

et al. 2023

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“…18 Soverini and Soderstrom proposed FIR models identification methods from a finite number of measurements. 19 Liu and Chen proposed the transformation-based distributed stochastic gradient algorithm for multivariate output-error systems. 20 Gu et al studied the hierarchical multi-innovation stochastic gradient identification algorithm for estimating a bilinear state-space model with moving average noise.…”

Section: Introductionmentioning

confidence: 99%

“…Diao et al studied the event‐triggered identification of FIR models 18 . Soverini and Soderstrom proposed FIR models identification methods from a finite number of measurements 19 . Liu and Chen proposed the transformation‐based distributed stochastic gradient algorithm for multivariate output‐error systems 20 .…”

Section: Introductionmentioning

confidence: 99%

A filtering‐based recursive extended least squares algorithm and its convergence for finite impulse response moving average systems

Zheng,

Ding

2024

Intl J Robust & Nonlinear

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This article considers the parameter identification problems of stochastic systems which described by the finite impulse response moving average model. Since the system is disturbed by colored noise, we introduce the data filtering technique from a view point of improving the parameter estimation accuracy. The data filtering technique is to use a filter to filter the input and output data of the system disturbed by colored noise so as to improve the identification accuracy. By using the data filtering technique, this article proposes a filtering‐based recursive extended least squares (F‐RELS) algorithm. The convergence analysis indicates that the parameter estimates can converge to their true values. Compared with the recursive extended least squares algorithm, the proposed F‐RELS algorithm can obtain more accurate parameter estimation. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed algorithms.

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“…The field of systems identification has recently become an active area of research that has attracted the attention of a considerable number of researchers [1][2][3][4][5]. The identification consists in searching the parameters of mathematical models of a system, based on experimental data and a priori available knowledge.…”

Section: Introductionmentioning

confidence: 99%

Channel Identification of Non-linear Systems with Binary-Valued Output Observations Based on Positive Definite Kernels

2021

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Nowadays, the kernel methods are increasingly developed, they are a significant source of advances, not only in terms of computational cost but also in terms of the obtained efficiencies in solving complex tasks, they are founded on the theory of reproducing kernel Hilbert spaces (RKHS). In this paper, we propose an algorithm for recursive identification of finite impulse response (FIR) nonlinear systems, whose outputs are detected by binary value sensors. This algorithm is based on a nonlinear transformation of the data using a kernel function. This transformation performs a basic change that allows the data to be projected into a new space where the relationships between the variables are linear. To test the accuracy of the proposed algorithm, we have compared it with another algorithm proposed in the literature, for that, we employ the practical frequency selective fading channel, called Broadband Radio Access Network (BRAN). Monte Carlo simulation results, in noisy environment and for various data length, demonstrate that the proposed algorithm can give better precision.

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Frequency domain identification of FIR models in the presence of additive input–output noise

Cited by 9 publications

References 25 publications

Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models

Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models

A filtering‐based recursive extended least squares algorithm and its convergence for finite impulse response moving average systems

Channel Identification of Non-linear Systems with Binary-Valued Output Observations Based on Positive Definite Kernels

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