Impulse response identification from input/output binary measurements

Pouliquen, Mathieu; Pigeon, Eric; Gehan, Olivier; Goudjil, Abdelhak; Auber, Romain

doi:10.1016/j.automatica.2020.109307

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…and A in (22). 1 Based on the above state-space representation, the evolutionary equation of x (k) for a given model parameter θ [h] and the related non-linear output equation are given by:…”

Section: Filteringmentioning

confidence: 99%

“…System identification from binary sensors is addressed in [15] for Finite Impulse Response (FIR) models under the (strong) assumption that that noise distribution is exactly known. Algorithms for identification of FIR models from binary input and output measurements have been recently presented in [16], [22]. Both of these contributions are based on the assumption that the (hidden) input is normally distributed.…”

Section: Introductionmentioning

confidence: 99%

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Learning Dynamical Systems From Quantized Observations: A Bayesian Perspective

Piga

Mejari

Forgione

2022

IEEE Trans. Automat. Contr.

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Identification of dynamical systems from lowresolution quantized observations presents several challenges because of the limited amount of information available in the data and since proper algorithms have to be designed to handle the error due to quantization. In this paper, we consider identification of Infinite Impulse Response (IIR) models from quantized outputs. Algorithms both for maximum-likelihood estimation and Bayesian inference are developed. Finally, a particle-filter approach is presented for recursive reconstruction of the latent non-quantized outputs from past quantized observations.

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Section: Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Dynamical Systems From Quantized Observations: A Bayesian Perspective

Piga

Mejari

Forgione

2022

IEEE Trans. Automat. Contr.

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“…Note that we consider here constant thresholds on quantizers. The proposed algorithms are an extension of the offline algorithm presented in [20]. The algorithms are based on the least mean squares (LMS) algorithm and the estimation of correlation and cross‐correlation function of the input and the output.…”

Section: Introductionmentioning

confidence: 99%

Recursive system identification method from binary input/output measurements

Oualla

Pouliquen

Frikel

et al. 2023

Asian Journal of Control

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This paper presents two online identification algorithms of finite impulse response (FIR) systems using binary measurements both on the input and on the output. These algorithms are based on the least mean square (LMS) technique and on the estimation of the correlation functions of the input and output from binary data. Note that the second algorithm is a simplified version of the first one in the case of a white noise on the input. The convergence and variance analyses are provided. A numerical example is given to demonstrate the effectiveness of the proposed algorithms.

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Continuous-Time Dynamic Model Identification Using Binary-Valued Observations of Input and Output Signals

Figwer

2023

Lecture Notes in Networks and Systems

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Impulse response identification from input/output binary measurements

Cited by 7 publications

References 14 publications

Learning Dynamical Systems From Quantized Observations: A Bayesian Perspective

Learning Dynamical Systems From Quantized Observations: A Bayesian Perspective

Recursive system identification method from binary input/output measurements

Continuous-Time Dynamic Model Identification Using Binary-Valued Observations of Input and Output Signals

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