Mixed<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>Estimators Variety for Model Order Reduction in Control Oriented System Identification

Corbier, Christophe; Carmona, Jean-Claude

doi:10.1155/2015/349070

Cited by 4 publications

(2 citation statements)

References 35 publications

(45 reference statements)

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“…Indeed, this remains in agreement with the formal point of view related to the bias and the new curve in Section 2.3: low values of γ involve reduced bias and improve the FIT of the reduced order model. In Corbier and Carmona [13] we showed that the Huberian model order denoted d…”

Section: Resultsmentioning

confidence: 93%

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Huberian approach for reduced order ARMA modeling of neurodegenerative disorder signal

2015

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International audienceThe purpose of this paper is to address the question of the existence of auto regressive moving average (ARMA) models with reduced order for neurodegenerative disorder signals by using Huberian approach. Since gait rhythm dynamics between Parkinson's disease (PD) or Huntington's disease (HD) and healthy control (CO) differ, and since the stride interval presents great variability, we propose a different ARMA modeling approach based on a Huberian function to assess parameters. Huberian function as a mixture of L 2 and L 1 norms, tuned with a threshold γ from a new curve, is chosen to deal with stride signal disorders. The choice of γ is crucial to ensure a good treatment of NO and allows to reduce the model order. The disorders induce disturbances in the classical estimation methods and increase of the number of parameters of the ARMA model. Here, the use of the Huberian function reduces the number of parameters of the estimated models leading to a disease transfer function with low order for PD and HD. Mathematical approach is discussed and experimental results based on a database containing 16 CO, 15 PD, and 19 HD are presented

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Section: Resultsmentioning

confidence: 93%

“…However, it remains to characterize more appreciably the diseases to differentiate the neurodegenerative disorders. Accordingly, future work will focus on mixed L p estimator [13] to reduce the number of parameters providing new indicators and will investigate the memory effect of human walking.…”

Section: Resultsmentioning

confidence: 99%

Huberian approach for reduced order ARMA modeling of neurodegenerative disorder signal

2015

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$$\mathbb {R}$$-Complex Finsler Information Geometry Applied to Manifolds of Systems

Corbier

2019

Lecture Notes in Computer Science

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Low-order control-oriented modeling of piezoelectric actuator using Huberian function with low threshold: pseudolinear and neural network models

Corbier

Ugalde

2016

Nonlinear Dyn

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International audienceThis paper presents a new methodology of nonlinear system identification using Huberian function. Pseudolinear and Neural network black box model families are applied to identify a piezoelectric actuator for suspensions and vibration assisted drilling. Huberian function, which is a mixture of L2L2L\₂ and L1L1L\₁ norms with a threshold, is used to estimate a parameters vector in these model families. Pseudolinear black box model with reduced model order and balanced simplicity-accuracy neural network model families are proposed. Moreover, we show the interest to decrease the threshold in the Huberian function by providing efficient models for vibration drilling control. Experimental results are presented and discusse

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MixedLpEstimators Variety for Model Order Reduction in Control Oriented System Identification

Cited by 4 publications

References 35 publications

Huberian approach for reduced order ARMA modeling of neurodegenerative disorder signal

Huberian approach for reduced order ARMA modeling of neurodegenerative disorder signal

$$\mathbb {R}$$-Complex Finsler Information Geometry Applied to Manifolds of Systems

Low-order control-oriented modeling of piezoelectric actuator using Huberian function with low threshold: pseudolinear and neural network models

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