Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using laguerre functions.

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using laguerre functions. / Chon, K H; Cohen, R J; Holstein-Rathlou, N H.

In: Annals of Biomedical Engineering, Vol. 25, No. 4, 1997, p. 731-8.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Chon, KH, Cohen, RJ & Holstein-Rathlou, NH 1997, 'Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using laguerre functions.', Annals of Biomedical Engineering, vol. 25, no. 4, pp. 731-8.

APA

Chon, K. H., Cohen, R. J., & Holstein-Rathlou, N. H. (1997). Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using laguerre functions. Annals of Biomedical Engineering, 25(4), 731-8.

Vancouver

Chon KH, Cohen RJ, Holstein-Rathlou NH. Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using laguerre functions. Annals of Biomedical Engineering. 1997;25(4):731-8.

Author

Chon, K H ; Cohen, R J ; Holstein-Rathlou, N H. / Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using laguerre functions. In: Annals of Biomedical Engineering. 1997 ; Vol. 25, No. 4. pp. 731-8.

Bibtex

@article{e2a90570ab6411ddb5e9000ea68e967b,
title = "Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using laguerre functions.",
abstract = "A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre function remain with our algorithm; but, by extending the algorithm to the linear and nonlinear ARMA model, a significant reduction in the number of Laguerre functions can be made, compared with the Volterra-Wiener approach. This translates into a more compact system representation and makes the physiological interpretation of higher order kernels easier. Furthermore, simulation results show better performance of the proposed approach in estimating the system dynamics than LEK in certain cases, and it remains effective in the presence of significant additive measurement noise.",
author = "Chon, {K H} and Cohen, {R J} and Holstein-Rathlou, {N H}",
note = "Keywords: Algorithms; Least-Squares Analysis; Linear Models; Models, Biological; Nonlinear Dynamics; Signal Processing, Computer-Assisted; Time Factors",
year = "1997",
language = "English",
volume = "25",
pages = "731--8",
journal = "Annals of Biomedical Engineering",
issn = "0090-6964",
publisher = "Springer",
number = "4",

}

RIS

TY - JOUR

T1 - Compact and accurate linear and nonlinear autoregressive moving average model parameter estimation using laguerre functions.

AU - Chon, K H

AU - Cohen, R J

AU - Holstein-Rathlou, N H

N1 - Keywords: Algorithms; Least-Squares Analysis; Linear Models; Models, Biological; Nonlinear Dynamics; Signal Processing, Computer-Assisted; Time Factors

PY - 1997

Y1 - 1997

N2 - A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre function remain with our algorithm; but, by extending the algorithm to the linear and nonlinear ARMA model, a significant reduction in the number of Laguerre functions can be made, compared with the Volterra-Wiener approach. This translates into a more compact system representation and makes the physiological interpretation of higher order kernels easier. Furthermore, simulation results show better performance of the proposed approach in estimating the system dynamics than LEK in certain cases, and it remains effective in the presence of significant additive measurement noise.

AB - A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre function remain with our algorithm; but, by extending the algorithm to the linear and nonlinear ARMA model, a significant reduction in the number of Laguerre functions can be made, compared with the Volterra-Wiener approach. This translates into a more compact system representation and makes the physiological interpretation of higher order kernels easier. Furthermore, simulation results show better performance of the proposed approach in estimating the system dynamics than LEK in certain cases, and it remains effective in the presence of significant additive measurement noise.

M3 - Journal article

C2 - 9236985

VL - 25

SP - 731

EP - 738

JO - Annals of Biomedical Engineering

JF - Annals of Biomedical Engineering

SN - 0090-6964

IS - 4

ER -

ID: 8420705