Matchable-observable linear models for multivariable identification: Structure selection and experimental results

Romano, Rodrigo Alvite; Pait, Felipe; Ferrão, Rafael Corsi

doi:10.1109/cdc.2015.7402730

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…Bad choices of A 0 will lead to inaccurate models while good choices lead to excellent models. From (18), it can be seen that if L = K , the PEM e y (k) becomes white noise.…”

Section: Multiple-input-single-output Moli Algorithmsmentioning

confidence: 99%

System Identification of Just Walk: Using Matchable-Observable Linear Parametrizations

Santos

Freigoun

Martín

et al. 2020

IEEE Trans. Contr. Syst. Technol.

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System identification approaches have been used to design an experiment, generate data, and estimate dynamical system models for Just Walk, a behavioral intervention intended to increase physical activity in sedentary adults. The estimated models serve a number of important purposes, such as understanding the factors that influence behavior and as the basis for using control systems as decision algorithms in optimized interventions. A class of identification algorithms known as matchable-observable linear identification has been reformulated and adapted to estimate linear time-invariant models from data obtained from this intervention. The experimental design, estimation algorithms, and validation procedures are described, with the best models estimated from data corresponding to an individual intervention participant. The results provide insights into the individual and the intervention, which can be used to improve the design of future studies.

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“…Bad choices of A 0 will lead to inaccurate models while good choices lead to excellent models. From (18), it can be seen that if L = K , the PEM e y (k) becomes white noise.…”

Section: Multiple-input-single-output Moli Algorithmsmentioning

confidence: 99%

System Identification of Just Walk: Using Matchable-Observable Linear Parametrizations

Santos

Freigoun

Martín

et al. 2020

IEEE Trans. Contr. Syst. Technol.

Self Cite

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Machine learning barycenter approach to identifying LPV state-space models

Romano

Santos

Pait

et al. 2016

2016 American Control Conference (ACC)

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In this paper an identification method for state-space LPV models is presented. The method is based on a particular parameterization that can be written in linear regression form and enables model estimation to be handled using Least-Squares Support Vector Machine (LS-SVM). The regression form has a set of design variables that act as filter poles to the underlying basis functions. In order to preserve the meaning of the Kernel functions (crucial in the LS-SVM context), these are filtered by a 2D-system with the predictor dynamics. A data-driven, direct optimization based approach for tuning this filter is proposed. The method is assessed using a simulated example and the results obtained are twofold. First, in spite of the difficult nonlinearities involved, the nonparametric algorithm was able to learn the underlying dependencies on the scheduling signal. Second, a significant improvement in the performance of the proposed method is registered, if compared with the one achieved by placing the predictor poles at the origin of the complex plane, which is equivalent to considering an estimator based on an LPV auto-regressive structure.

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Recursive identification of multivariable systems using matchable-observable linear models

Romano

Pait

2016

2016 IEEE Conference on Control Applications (CCA)

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Matchable-observable linear models for multivariable identification: Structure selection and experimental results

Cited by 6 publications

References 15 publications

System Identification of Just Walk: Using Matchable-Observable Linear Parametrizations

System Identification of Just Walk: Using Matchable-Observable Linear Parametrizations

Machine learning barycenter approach to identifying LPV state-space models

Recursive identification of multivariable systems using matchable-observable linear models

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