impulseest: A Python package for non-parametric impulse response estimation with input–output data

Luan Vinícius Fiorio; Chrystian Lenon Remes; Yales Rômulo de Novaes

doi:10.1016/j.softx.2021.100761

impulseest: A Python package for non-parametric impulse response estimation with input–output data

Luan Vinícius Fiorio (Corresponding author), Chrystian Lenon Remes, Yales Rômulo de Novaes

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

This paper presents the impulseest Python package, used for estimating the impulse response of a system relying solely on input and output data. This package can provide estimates in a non-parametric fashion either with regularization techniques. For the regularized estimates, impulseest function uses the Empirical Bayes method. On the other hand, the non-regularized case is solved through the least squares algorithm. This function is tested considering an experimental situation, several dynamic processes and also through Monte Carlo simulations. The obtained results are analyzed mainly in terms of the Mean Square Error (MSE), besides other quantities. Through those results, it is shown that the impulseest function with regularization using the proposed regularization kernels leads to low MSE for all tested cases.

Original language	English
Article number	100761
Number of pages	7
Journal	SoftwareX
Volume	15
DOIs	https://doi.org/10.1016/j.softx.2021.100761
Publication status	Published - Jul 2021
Externally published	Yes

Bibliographical note

Funding Information:
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and partly by the Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina (FAPESC) - Grant number 8209/2021 .

Keywords

Impulse response
Estimation
Regularization

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title = "impulseest: A Python package for non-parametric impulse response estimation with input–output data",

abstract = "This paper presents the impulseest Python package, used for estimating the impulse response of a system relying solely on input and output data. This package can provide estimates in a non-parametric fashion either with regularization techniques. For the regularized estimates, impulseest function uses the Empirical Bayes method. On the other hand, the non-regularized case is solved through the least squares algorithm. This function is tested considering an experimental situation, several dynamic processes and also through Monte Carlo simulations. The obtained results are analyzed mainly in terms of the Mean Square Error (MSE), besides other quantities. Through those results, it is shown that the impulseest function with regularization using the proposed regularization kernels leads to low MSE for all tested cases.",

keywords = "Impulse response, Estimation, Regularization",

author = "Fiorio, {Luan Vin{\'i}cius} and Remes, {Chrystian Lenon} and {de Novaes}, {Yales R{\^o}mulo}",

note = "Funding Information: This study was financed in part by the Coordena{\c c}{\~a}o de Aperfei{\c c}oamento de Pessoal de N{\'i}vel Superior - Brasil (CAPES) - Finance Code 001 and partly by the Funda{\c c}{\~a}o de Amparo {\`a} Pesquisa e Inova{\c c}{\~a}o do Estado de Santa Catarina (FAPESC) - Grant number 8209/2021 . ",

year = "2021",

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doi = "10.1016/j.softx.2021.100761",

language = "English",

volume = "15",

journal = "SoftwareX",

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AU - Fiorio, Luan Vinícius

AU - Remes, Chrystian Lenon

AU - de Novaes, Yales Rômulo

N1 - Funding Information: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and partly by the Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina (FAPESC) - Grant number 8209/2021 .

PY - 2021/7

Y1 - 2021/7

N2 - This paper presents the impulseest Python package, used for estimating the impulse response of a system relying solely on input and output data. This package can provide estimates in a non-parametric fashion either with regularization techniques. For the regularized estimates, impulseest function uses the Empirical Bayes method. On the other hand, the non-regularized case is solved through the least squares algorithm. This function is tested considering an experimental situation, several dynamic processes and also through Monte Carlo simulations. The obtained results are analyzed mainly in terms of the Mean Square Error (MSE), besides other quantities. Through those results, it is shown that the impulseest function with regularization using the proposed regularization kernels leads to low MSE for all tested cases.

AB - This paper presents the impulseest Python package, used for estimating the impulse response of a system relying solely on input and output data. This package can provide estimates in a non-parametric fashion either with regularization techniques. For the regularized estimates, impulseest function uses the Empirical Bayes method. On the other hand, the non-regularized case is solved through the least squares algorithm. This function is tested considering an experimental situation, several dynamic processes and also through Monte Carlo simulations. The obtained results are analyzed mainly in terms of the Mean Square Error (MSE), besides other quantities. Through those results, it is shown that the impulseest function with regularization using the proposed regularization kernels leads to low MSE for all tested cases.

KW - Impulse response

KW - Estimation

KW - Regularization

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VL - 15

JO - SoftwareX

JF - SoftwareX

M1 - 100761

ER -

impulseest: A Python package for non-parametric impulse response estimation with input–output data

Abstract

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Keywords

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