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Journal articles on the topic 'Sampled-data models'

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Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Goodwin, Graham C., Juan I. Yuz, and Juan C. Agüero. "Relative Error Issues in Sampled Data Models." IFAC Proceedings Volumes 41, no. 2 (2008): 5047–52. http://dx.doi.org/10.3182/20080706-5-kr-1001.00848.

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2

Yuz, Juan I., and Graham C. Goodwin. "SAMPLED-DATA MODELS FOR STOCHASTIC NONLINEAR SYSTEMS." IFAC Proceedings Volumes 39, no. 1 (2006): 434–39. http://dx.doi.org/10.3182/20060329-3-au-2901.00065.

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3

Yiin, Lih-Huah, and H. Vincent Poor. "Linear interpolation models for rapidly-sampled data." Communications in Statistics - Theory and Methods 14, no. 4 (1998): 867–82. http://dx.doi.org/10.1080/03610929808828953.

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4

Yuz, J. I., and G. C. Goodwin. "On sampled-data models for nonlinear systems." IEEE Transactions on Automatic Control 50, no. 10 (October 2005): 1477–89. http://dx.doi.org/10.1109/tac.2005.856640.

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5

Yiin, Lih-Huah, and H. Vincent Poor. "Linear interpolation models for rapidly-sampled data." Communications in Statistics. Stochastic Models 14, no. 4 (January 1998): 867–82. http://dx.doi.org/10.1080/15326349808807505.

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6

Radchenko, Peter, Xinghao Qiao, and Gareth M. James. "Index Models for Sparsely Sampled Functional Data." Journal of the American Statistical Association 110, no. 510 (April 3, 2015): 824–36. http://dx.doi.org/10.1080/01621459.2014.931859.

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7

Yucra, Eduardo A., and Juan I. Yuz. "Frequency domain accuracy of approximate sampled-data models." IFAC Proceedings Volumes 44, no. 1 (January 2011): 8711–17. http://dx.doi.org/10.3182/20110828-6-it-1002.02257.

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8

Moheimani, S. O. Reza. "Model Correction for Sampled-Data Models of Structures." Journal of Guidance, Control, and Dynamics 24, no. 3 (May 2001): 634–37. http://dx.doi.org/10.2514/2.4760.

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9

Rabbath, C. A., N. Hori, and N. Lechevin. "Convergence of Sampled-Data Models in Digital Redesign." IEEE Transactions on Automatic Control 49, no. 5 (May 2004): 850–55. http://dx.doi.org/10.1109/tac.2004.828312.

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10

Wang, Jiandong, Tongwen Chen, and Biao Huang. "Multirate sampled-data systems: computing fast-rate models." Journal of Process Control 14, no. 1 (February 2004): 79–88. http://dx.doi.org/10.1016/s0959-1524(03)00033-7.

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11

Condat, Laurent, and Dimitri Van De Ville. "Quasi-Interpolating Spline Models for Hexagonally-Sampled Data." IEEE Transactions on Image Processing 16, no. 5 (May 2007): 1195–206. http://dx.doi.org/10.1109/tip.2007.891808.

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12

Broersen, Piet M. T., Robert Bos, and Stijn de Waele. "SPECTRAL ANALYSIS OF IRREGULARLY SAMPLED DATA WITH AUTOREGRESSIVE MODELS." IFAC Proceedings Volumes 35, no. 1 (2002): 347–52. http://dx.doi.org/10.3182/20020721-6-es-1901.01129.

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13

Bai, Er-Wei, and Zhi Ding. "Zeros of sampled data systems represented by FIR models." Automatica 36, no. 1 (January 2000): 121–23. http://dx.doi.org/10.1016/s0005-1098(99)00109-0.

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14

Tabouy, Timothée, Pierre Barbillon, and Julien Chiquet. "Variational Inference for Stochastic Block Models From Sampled Data." Journal of the American Statistical Association 115, no. 529 (April 11, 2019): 455–66. http://dx.doi.org/10.1080/01621459.2018.1562934.

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15

Broersen, Piet M. T. "Spectral Analysis of Irregularly Sampled Data with Time Series Models." Open Signal Processing Journal 1, no. 1 (January 2, 2009): 7–14. http://dx.doi.org/10.2174/1876825300801010007.

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16

Larsson, E. K., M. Mossberg, and T. Soderstrom. "Identification of Continuous-Time ARX Models From Irregularly Sampled Data." IEEE Transactions on Automatic Control 52, no. 3 (March 2007): 417–27. http://dx.doi.org/10.1109/tac.2007.892374.

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17

Koehly, Laura M., Steven M. Goodreau, and Martina Morris. "6. Exponential Family Models for Sampled and Census Network Data." Sociological Methodology 34, no. 1 (December 2004): 241–70. http://dx.doi.org/10.1111/j.0081-1750.2004.00153.x.

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18

Cederbaum, Jona, Marianne Pouplier, Phil Hoole, and Sonja Greven. "Functional linear mixed models for irregularly or sparsely sampled data." Statistical Modelling: An International Journal 16, no. 1 (December 31, 2015): 67–88. http://dx.doi.org/10.1177/1471082x15617594.

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19

Carrasco, Diego S., Graham C. Goodwin, and Juan I. Yuz. "Vector Measures of Accuracy for Sampled Data Models of Nonlinear Systems." IEEE Transactions on Automatic Control 58, no. 1 (January 2013): 224–30. http://dx.doi.org/10.1109/tac.2012.2203061.

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20

Åkesson, Bernt M., and Hannu T. Toivonen. "IDENTIFICATION OF QUASI-ARMAX MODELS OF NONLINEAR STOCHASTIC SAMPLED-DATA SYSTEMS." IFAC Proceedings Volumes 38, no. 1 (2005): 41–46. http://dx.doi.org/10.3182/20050703-6-cz-1902.00007.

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21

Gillberg, Jonas, and Lennart Ljung. "FREQUENCY-DOMAIN IDENTIFICATION OF CONTINUOUS-TIME ARMA MODELS FROM SAMPLED DATA." IFAC Proceedings Volumes 38, no. 1 (2005): 225–30. http://dx.doi.org/10.3182/20050703-6-cz-1902.00038.

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22

Huliehel, F., and S. Ben-Yaakov. "Low-frequency sampled-data models of switched mode DC-DC converters." IEEE Transactions on Power Electronics 6, no. 1 (January 1991): 55–61. http://dx.doi.org/10.1109/63.65003.

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23

Ilchmann, Achim, Stuart Townley, and Mosalagae Thuto. "Adaptive sampled-data tracking for input-constrained exothermic chemical reaction models." Systems & Control Letters 54, no. 12 (December 2005): 1149–61. http://dx.doi.org/10.1016/j.sysconle.2005.04.004.

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24

Gillberg, Jonas, and Lennart Ljung. "Frequency-domain identification of continuous-time ARMA models from sampled data." Automatica 45, no. 6 (June 2009): 1371–78. http://dx.doi.org/10.1016/j.automatica.2009.01.016.

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25

Owens, D. "First-order models for slow sampled-data multivariable systems: An asymptotic analysis." IEEE Transactions on Automatic Control 31, no. 6 (June 1986): 564–67. http://dx.doi.org/10.1109/tac.1986.1104340.

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26

Gillberg, Jonas, and Lennart Ljung. "FREQUENCY-DOMAIN IDENTIFICATION OF CONTINUOUS-TIME OUTPUT ERROR MODELS FROM SAMPLED DATA." IFAC Proceedings Volumes 38, no. 1 (2005): 95–100. http://dx.doi.org/10.3182/20050703-6-cz-1902.00016.

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27

Arcak, Murat, and Dragan Nešić. "NONLINEAR SAMPLED-DATA OBSERVER DESIGN VIA APPROXIMATE DISCRETE-TIME MODELS AND EMULATION." IFAC Proceedings Volumes 38, no. 1 (2005): 13–18. http://dx.doi.org/10.3182/20050703-6-cz-1902.00656.

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28

Chen, Fengwei, Juan C. Agüero, Marion Gilson, Hugues Garnier, and Tao Liu. "EM-based identification of continuous-time ARMA Models from irregularly sampled data." Automatica 77 (March 2017): 293–301. http://dx.doi.org/10.1016/j.automatica.2016.11.020.

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29

Qing-Guo Wang. "Identification of linearized continuous-time models of mechanical systems from sampled data." Computers in Industry 23, no. 3 (December 1993): 235–41. http://dx.doi.org/10.1016/0166-3615(93)90010-x.

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30

Kirshner, Hagai, Michael Unser, and John Paul Ward. "On the Unique Identification of Continuous-Time Autoregressive Models From Sampled Data." IEEE Transactions on Signal Processing 62, no. 6 (March 2014): 1361–76. http://dx.doi.org/10.1109/tsp.2013.2296879.

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31

Mariadassou, Mahendra, and Timothée Tabouy. "Consistency and asymptotic normality of stochastic block models estimators from sampled data." Electronic Journal of Statistics 14, no. 2 (2020): 3672–704. http://dx.doi.org/10.1214/20-ejs1750.

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32

Belcher, J., J. S. Hampton, and G. Tunnicliffe Wilson. "Parameterization of Continuous Time Autoregressive Models for Irregularly Sampled Time Series Data." Journal of the Royal Statistical Society: Series B (Methodological) 56, no. 1 (January 1994): 141–55. http://dx.doi.org/10.1111/j.2517-6161.1994.tb01966.x.

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33

Baldi, Simone, Shuai Yuan, Petr Endel, and Ondrej Holub. "Dual estimation: Constructing building energy models from data sampled at low rate." Applied Energy 169 (May 2016): 81–92. http://dx.doi.org/10.1016/j.apenergy.2016.02.019.

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34

Huang, Ji, Yang Shi, Haining Huang, and Zheng Li. "filtering for multirate nonlinear sampled-data systems using T-S fuzzy models." Digital Signal Processing 23, no. 1 (January 2013): 418–26. http://dx.doi.org/10.1016/j.dsp.2012.08.009.

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35

Halevi, Yoram, and Natalya Raskin. "Discrete-time frequency weighted model reduction and application to sampled-data models." Optimal Control Applications and Methods 19, no. 5 (September 1998): 315–29. http://dx.doi.org/10.1002/(sici)1099-1514(199809/10)19:5<315::aid-oca632>3.0.co;2-e.

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36

Oliva, E., G. Berselli, and F. Pini. "Dynamic Identification of Industrial Robots from Low-Sampled Data." Applied Mechanics and Materials 328 (June 2013): 644–50. http://dx.doi.org/10.4028/www.scientific.net/amm.328.644.

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Abstract:

This paper proposes a fast and on-site method for the dynamic identification of industrial robots from low-sampled position and torque data. Owing to the basic architecture of the employed controller, only trapezoidal-velocity trajectories can be enforced for identification purposes. Differently from previous literature, where this kind of trajectories were performed with limited joint velocities and range of motions, the procedure proposed hereafter is characterized by fast movements performed on wide angular ranges. Furthermore, in order to identify the influence of friction without deriving complex friction models, a novel method is outlined that decouples frictional torques from gravitational, centrifugal and inertial ones. Finally, although multiple experiments of different kinds have been performed, inertial parameters are determined in one singular step, thus avoiding possible error increase due to sequential identification algorithms.

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37

Chen, Fengwei, Hugues Garnier, Arturo Padilla, and Marion Gilson. "Recursive IV Identification of Continuous-Time Models With Time Delay From Sampled Data." IEEE Transactions on Control Systems Technology 28, no. 3 (May 2020): 1074–82. http://dx.doi.org/10.1109/tcst.2019.2896124.

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38

Venkataramanan, L., R. Kuc, and F. J. Sigworth. "Identification of hidden Markov models for ion channel currents .III. Bandlimited, sampled data." IEEE Transactions on Signal Processing 48, no. 2 (2000): 376–85. http://dx.doi.org/10.1109/78.823965.

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39

Espeland, Mark A., and Charles L. Odoroff. "Log-Linear Models for Doubly Sampled Categorical Data Fitted by the EM Algorithm." Journal of the American Statistical Association 80, no. 391 (September 1985): 663–70. http://dx.doi.org/10.1080/01621459.1985.10478166.

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40

Broersen, P. M. T. "The Removal of Spurious Spectral Peaks From Autoregressive Models for Irregularly Sampled Data." IEEE Transactions on Instrumentation and Measurement 59, no. 1 (January 2010): 205–14. http://dx.doi.org/10.1109/tim.2009.2022451.

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41

Yuz, J. I., J. Alfaro, J. C. Agüero, and G. C. Goodwin. "Identification of continuous-time state-space models from non-uniform fast-sampled data." IET Control Theory & Applications 5, no. 7 (May 5, 2011): 842–55. http://dx.doi.org/10.1049/iet-cta.2010.0246.

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42

Carrasco, Diego S., Graham C. Goodwin, and Juan I. Yuz. "Sampled Data Models for Nonlinear Stochastic Systems: Truncation Errors and Sampling Zero Dynamics." IEEE Transactions on Automatic Control 61, no. 1 (January 2016): 103–15. http://dx.doi.org/10.1109/tac.2015.2433572.

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43

Andrade, Valdir Carlos Lima de, and Helio Garcia Leite. "Hipsometric relationship modeling using data sampled in tree scaling and inventory plots." Revista Árvore 35, no. 1 (February 2011): 157–64. http://dx.doi.org/10.1590/s0100-67622011000100019.

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This work evaluated eight hypsometric models to represent tree height-diameter relationship, using data obtained from the scaling of 118 trees and 25 inventory plots. Residue graphic analysis and percent deviation mean criteria, qui-square test precision, residual standard error between real and estimated heights and the graybill f test were adopted. The identity of the hypsometric models was also verified by applying the F(Ho) test on the plot data grouped to the scaling data. It was concluded that better accuracy can be obtained by using the model prodan, with h and d1,3 data measured in 10 trees by plots grouped into these scaling data measurements of even-aged forest stands.

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44

Liaw, C. M., M. Ouyang, and C. T. Pan. "Reduced-Order Parameter Estimation for Continuous Systems From Sampled Data." Journal of Dynamic Systems, Measurement, and Control 112, no. 2 (June 1, 1990): 305–8. http://dx.doi.org/10.1115/1.2896140.

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An indirect method for estimating the parameters of the reduced continuous-time model from the sampled input/output data is presented. In this method, a discrete-time ARMA model is first identified. Then, the order of the continuous-time model is minimized by the dispersion analysis and/or accumulated dispersion analysis with the criterion of minimum discrepancy in sense of energy contribution between the original system and the reduced model. Finally, the reduced continuous-time model is matched to the identified discrete ARMA model in frequency domain. The proposed approach is applied to the identification of a power system stabilizer. The results show that the estimated continuous-time models are rather close to those supplied by the vender.

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45

Lopes, Sofia Moreira de Andrade, Rogério Andrade Flauzino, and Ruy Alberto Corrêa Altafim. "Incipient fault diagnosis in power transformers by data-driven models with over-sampled dataset." Electric Power Systems Research 201 (December 2021): 107519. http://dx.doi.org/10.1016/j.epsr.2021.107519.

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46

Qin, Pan, Shunshoku Kanae, Zi-Jiang Yang, and Kiyoshi Wada. "Identification of Lifted Models for General Dual-Rate Sampled-Data Systems Using N4SID Algorithm." IEEJ Transactions on Electronics, Information and Systems 128, no. 5 (2008): 788–94. http://dx.doi.org/10.1541/ieejeiss.128.788.

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47

Gillberg, Jonas, and Lennart Ljung. "FREQUENCY-DOMAIN IDENTIFICATION OF CONTINUOUS-TIME OUTPUT ERROR MODELS FROM NON-UNIFORMLY SAMPLED DATA." IFAC Proceedings Volumes 39, no. 1 (2006): 214–18. http://dx.doi.org/10.3182/20060329-3-au-2901.00028.

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48

Broersen, P. "Practical Aspects of the Spectral Analysis of Irregularly Sampled Data With Time-Series Models." IEEE Transactions on Instrumentation and Measurement 58, no. 5 (May 2009): 1380–88. http://dx.doi.org/10.1109/tim.2008.2009201.

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49

Grüne, Lars, and Dragan Nesic. "Optimization-Based Stabilization of Sampled-Data Nonlinear Systems via Their Approximate Discrete-Time Models." SIAM Journal on Control and Optimization 42, no. 1 (January 2003): 98–122. http://dx.doi.org/10.1137/s036301290240258x.

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50

Chen, Fengwei, Hugues Garnier, and Marion Gilson. "Robust identification of continuous-time models with arbitrary time-delay from irregularly sampled data." Journal of Process Control 25 (January 2015): 19–27. http://dx.doi.org/10.1016/j.jprocont.2014.10.003.

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