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Journal articles on the topic 'Recursive identification'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Park, H. M., and W. J. Lee. "Recursive Identification of Thermal Convection." Journal of Dynamic Systems, Measurement, and Control 125, no. 1 (2003): 1–10. http://dx.doi.org/10.1115/1.1540116.

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A method is developed for the recursive identification of thermal convection system governed by the Boussinesq equation using an extended Kalman filter. A computationally feasible Kalman filter is constructed by reducing the Boussinesq equation to a small number of ordinary differential equations by means of the Karhunen-Loe`ve Galerkin procedure which is a type of Galerkin method employing the empirical eigenfunctions of the Karhunen-Loe`ve decomposition. Employing the Kalman filter constructed by using the reduced order model, the thermal convection induced by a spatially varying heat flux at the bottom is identified recursively by using either the Boussinesq equation or the reduced order model itself. The recursive identification technique developed in the present work is found to yield accurate results for thermal convection even with approximate covariance equation and noisy measurements. It is also shown that a reasonably accurate and computationally feasible method of recursive identification can be constructed even with a relatively inaccurate reduced order model.

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2

Chen, Han-Fu. "Recursive system identification." Acta Mathematica Scientia 29, no. 3 (2009): 650–72. http://dx.doi.org/10.1016/s0252-9602(09)60062-x.

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3

El-Sheikh, Garnal. "Recursive Identification Methods." International Conference on Aerospace Sciences and Aviation Technology 7, ASAT CONFERENCE (1997): 1–9. http://dx.doi.org/10.21608/asat.1997.25437.

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4

Ljung, Lennart. "Recursive identification algorithms." Circuits, Systems, and Signal Processing 21, no. 1 (2002): 57–68. http://dx.doi.org/10.1007/bf01211651.

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5

Wani Jamaludin, Irma Wani Jamaludin, and Norhaliza Abdul Wahab. "Recursive Subspace Identification Algorithm using the Propagator Based Method." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 1 (2017): 172. http://dx.doi.org/10.11591/ijeecs.v6.i1.pp172-179.

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<p>Subspace model identification (SMI) method is the effective method in identifying dynamic state space linear multivariable systems and it can be obtained directly from the input and output data. Basically, subspace identifications are based on algorithms from numerical algebras which are the QR decomposition and Singular Value Decomposition (SVD). In industrial applications, it is essential to have online recursive subspace algorithms for model identification where the parameters can vary in time. However, because of the SVD computational complexity that involved in the algorithm, the classical SMI algorithms are not suitable for online application. Hence, it is essential to discover the alternative algorithms in order to apply the concept of subspace identification recursively. In this paper, the recursive subspace identification algorithm based on the propagator method which avoids the SVD computation is proposed. The output from Numerical Subspace State Space System Identification (N4SID) and Multivariable Output Error State Space (MOESP) methods are also included in this paper.</p>

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6

Yan, Zheping, Di Wu, Jiajia Zhou, and Lichao Hao. "Recursive Subspace Identification of AUV Dynamic Model under General Noise Assumption." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/547539.

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A recursive subspace identification algorithm for autonomous underwater vehicles (AUVs) is proposed in this paper. Due to the advantages at handling nonlinearities and couplings, the AUV model investigated here is for the first time constructed as a Hammerstein model with nonlinear feedback in the linear part. To better take the environment and sensor noises into consideration, the identification problem is concerned as an errors-in-variables (EIV) one which means that the identification procedure is under general noise assumption. In order to make the algorithm recursively, propagator method (PM) based subspace approach is extended into EIV framework to form the recursive identification method called PM-EIV algorithm. With several identification experiments carried out by the AUV simulation platform, the proposed algorithm demonstrates its effectiveness and feasibility.

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7

Feng, Xu, Ching-Fang Lin, and Norman P. Coleman. "Frequency-Domain Recursive Robust Identification." Journal of Guidance, Control, and Dynamics 23, no. 5 (2000): 908–10. http://dx.doi.org/10.2514/2.4628.

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8

Le, Fengmin, Ivan Markovsky, Christopher Freeman, and Eric Rogers. "Recursive Identification of Hammerstein Systems." IFAC Proceedings Volumes 44, no. 1 (2011): 13954–59. http://dx.doi.org/10.3182/20110828-6-it-1002.00313.

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9

Unton, F. Z. "A New Recursive Identification Technique." IFAC Proceedings Volumes 18, no. 5 (1985): 873–78. http://dx.doi.org/10.1016/s1474-6670(17)60671-2.

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10

Sanchis, R., and P. Albertos. "Recursive Identification under Scarce Measurements." IFAC Proceedings Volumes 33, no. 15 (2000): 745–50. http://dx.doi.org/10.1016/s1474-6670(17)39841-5.

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11

Xia, L., V. Krishnamurthy, and J. B. Moore. "Recursive Identification of Overparametrized Systems." IFAC Proceedings Volumes 23, no. 1 (1990): 433–36. http://dx.doi.org/10.1016/s1474-6670(17)52759-7.

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12

Wigren, T. "Circle criteria in recursive identification." IEEE Transactions on Automatic Control 42, no. 7 (1997): 975–79. http://dx.doi.org/10.1109/9.599976.

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13

Xia, L., and J. B. Moore. "Recursive identification of overparametrized systems." IEEE Transactions on Automatic Control 34, no. 3 (1989): 327–31. http://dx.doi.org/10.1109/9.16425.

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14

Samann, M. A., D. Mortari, and J. L. Junkins. "Recursive mode star identification algorithms." IEEE Transactions on Aerospace and Electronic Systems 41, no. 4 (2005): 1246–54. http://dx.doi.org/10.1109/taes.2005.1561885.

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15

FNAIECH, FARHAT, and LENNART LJUNG. "Recursive identification of bilinear systems." International Journal of Control 45, no. 2 (1987): 453–70. http://dx.doi.org/10.1080/00207178708933743.

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16

Hapfelmeier, Alexander, Kurt Ulm, and Bernhard Haller. "Subgroup identification by recursive segmentation." Journal of Applied Statistics 45, no. 15 (2018): 2864–87. http://dx.doi.org/10.1080/02664763.2018.1444152.

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17

Szabłowski, P. J. "Optimal, recursive procedures of identification." Computers & Mathematics with Applications 16, no. 3 (1988): 229–46. http://dx.doi.org/10.1016/0898-1221(88)90183-6.

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18

Valcke, Christian P., J. Steven Jenkins, and Denham S. Ward. "Recursive identification of lung parameters." Computer Methods and Programs in Biomedicine 29, no. 2 (1989): 143–49. http://dx.doi.org/10.1016/0169-2607(89)90081-3.

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19

Li, Yu Ying, Huan Li Zhang, Shi Liang Zhang, and Wei Rong Chen. "Generation and Identification for F-Recursive Genetic Information." Applied Mechanics and Materials 475-476 (December 2013): 1055–59. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.1055.

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The P-sets consists of an internal P-set and an outer P-set . It has dynamic characteristics. In order to obtain a new method of generation and identification information, the paper proposes the concepts of-recursive genetic information and-recursive genetic degree by using the dynamic characteristic of the internal P-set. Also the structure and characteristics about-recursive genetic information are given, including the the identification theorems and the identification criterions for the-recursive genetic information. Finally, the application example of the-recursive genetic information is provided. The structure of the-recursive genetic information is a new mathematical tool to process informaiton that has inward contraction characteristic.

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20

Dai, Zhi Hua, Yu An Pan, and Jie Yao. "Parameters Recursive Identification of Minimum Variance Control." Applied Mechanics and Materials 347-350 (August 2013): 15–18. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.15.

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we discuss the problem of parameters recursive identification and designing of optimal input signal for minimum variance control from the point of system identification. we propose multi-innovation recursive least-squares identification method and separable iterative recursive least-squares identification method to identify and estimate it on line. Finally, the efficiency and possibility of the proposed strategy can be confirmed by the simulation example results.

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21

Ni, Zhiyu, Jinguo Liu, and Zhigang Wu. "Identification of the time-varying modal parameters of a spacecraft with flexible appendages using a recursive predictor-based subspace identification algorithm." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 6 (2018): 2032–50. http://dx.doi.org/10.1177/0954410018770560.

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This study focuses on the recursive identification of the time-varying modal parameters of on-orbit spacecraft caused by structural configuration changes. For this purpose, an algorithm called recursive predictor-based subspace identification is applied as an alternative method to improve the computational efficiency and noise robustness, and to implement an online identification of system parameters. In the existing time-domain identification methods, the eigensystem realization algorithm and subspace identification methods are usually applied to obtain the on-orbit spacecraft modal parameters. However, these approaches are designed based on a time-invariant system and singular value decomposition, which require a significant amount of computational time. Thus, these methods are difficult to employ for online identification. According to the adaptive filter theory, the recursive predictor-based subspace identification algorithm can not only avoid the singular value decomposition computation but also provide unbiased estimates in a general noisy framework using the recursive least squares approach. Furthermore, in comparison with the classical projection approximation subspace tracking series recursive algorithm, the recursive predictor-based subspace identification method is more suitable for systems with strong noise disturbances. By establishing the dynamics model of a large rigid-flexible coupling spacecraft, three cases of on-orbit modal parameter variation with time are investigated, and the corresponding system frequencies are identified using the recursive predictor-based subspace identification, projection approximation subspace tracking, and singular value decomposition methods. The results demonstrate that the recursive predictor-based subspace identification algorithm can be used to effectively perform an online parameter identification, and the corresponding computational efficiency and noise robustness are better than those of the singular value decomposition and projection approximation subspace tracking series approaches, respectively. Finally, the applicability of this method is also verified through a numerical simulation.

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22

Zhang, Xiangping, Yucai Zhu, and Paul Van Den Bosch. "Improve Recursive Identification using Multi-Iteration." IFAC Proceedings Volumes 42, no. 10 (2009): 444–49. http://dx.doi.org/10.3182/20090706-3-fr-2004.00073.

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23

Koubaa, Yassine. "Recursive identification of induction motor parameters." Simulation Modelling Practice and Theory 12, no. 5 (2004): 363–81. http://dx.doi.org/10.1016/j.simpat.2004.04.003.

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24

Prüher, Jakub, and Miroslav Šimandl. "Gaussian process based recursive system identification." Journal of Physics: Conference Series 570, no. 1 (2014): 012002. http://dx.doi.org/10.1088/1742-6596/570/1/012002.

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25

LEVIN, ASRIEL U., and KUMPATI S. NARENDRA. "Recursive identification using feedforward neural networks." International Journal of Control 61, no. 3 (1995): 533–47. http://dx.doi.org/10.1080/00207179508921916.

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26

SOLBRAND, GÖTE, ANDÉRS AHLEN, and LENNART LJUNG. "Recursive methods for off-line identification." International Journal of Control 41, no. 1 (1985): 177–91. http://dx.doi.org/10.1080/0020718508961119.

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27

Kumar, P. "Theory and practice of recursive identification." IEEE Transactions on Automatic Control 30, no. 10 (1985): 1054–56. http://dx.doi.org/10.1109/tac.1985.1103802.

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28

Filipovic, V. Z., and B. D. Kovacevic. "Analysis of Robust Recursive Identification Algorithms." IFAC Proceedings Volumes 20, no. 5 (1987): 321–26. http://dx.doi.org/10.1016/s1474-6670(17)55520-2.

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29

O'BANNON, G., and L. W. BEZANSON. "COMPARISON OF IMPROVED RECURSIVE IDENTIFICATION ALGORITHMS." Chemical Engineering Communications 99, no. 1 (1991): 1–13. http://dx.doi.org/10.1080/00986449108911574.

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30

Dréano, P., J. Fantini, and R. Laurent. "Recursive Identification of Continuous Bilinear Systems." IFAC Proceedings Volumes 30, no. 6 (1997): 497–501. http://dx.doi.org/10.1016/s1474-6670(17)43413-6.

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31

Skibinski, G. L., and W. A. Sethares. "Thermal parameter estimation using recursive identification." IEEE Transactions on Power Electronics 6, no. 2 (1991): 228–39. http://dx.doi.org/10.1109/63.76809.

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32

EMARA-SHABAIK, Hosam E., Mohammed S. AHMED, and Khaled H. AL-AJMI. "Wiener-Hammerstein Model Identification-Recursive Algorithms." JSME International Journal Series C 45, no. 2 (2002): 606–13. http://dx.doi.org/10.1299/jsmec.45.606.

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33

Chen, Han-Fu. "Recursive system identification by stochastic approximation." Communications in Information and Systems 6, no. 4 (2006): 253–72. http://dx.doi.org/10.4310/cis.2006.v6.n4.a1.

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34

Chen, Xing-Min, and Han-Fu Chen. "Recursive Identification for MIMO Hammerstein Systems." IEEE Transactions on Automatic Control 56, no. 4 (2011): 895–902. http://dx.doi.org/10.1109/tac.2010.2101691.

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35

Mu, Bi-Qiang, and Han-Fu Chen. "Recursive Identification of MIMO Wiener Systems." IEEE Transactions on Automatic Control 58, no. 3 (2013): 802–8. http://dx.doi.org/10.1109/tac.2012.2215539.

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36

Mu, Bi-Qiang, and Han-Fu Chen. "Recursive Identification of Wiener--Hammerstein Systems." SIAM Journal on Control and Optimization 50, no. 5 (2012): 2621–58. http://dx.doi.org/10.1137/110826564.

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37

Hunt, K. J. "A survey of recursive identification algorithms." Transactions of the Institute of Measurement and Control 8, no. 5 (1986): 273–78. http://dx.doi.org/10.1177/014233128600800505.

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38

Chen, Han-Fu. "Recursive Identification of EIV ARMA Processes." IFAC Proceedings Volumes 41, no. 2 (2008): 1366–71. http://dx.doi.org/10.3182/20080706-5-kr-1001.00234.

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39

Vidal, René. "Recursive identification of switched ARX systems." Automatica 44, no. 9 (2008): 2274–87. http://dx.doi.org/10.1016/j.automatica.2008.01.025.

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40

Greblicki, Wlodzimierz, and Miroslaw Pawlak. "Recursive nonparametric identification of Hammerstein systems." Journal of the Franklin Institute 326, no. 4 (1989): 461–81. http://dx.doi.org/10.1016/0016-0032(89)90045-8.

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41

Chen, HanFu. "Recursive identification for EIV ARMAX systems." Science in China Series F: Information Sciences 52, no. 11 (2009): 1964–72. http://dx.doi.org/10.1007/s11432-009-0195-5.

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42

Telsang, B., S. T. Navalkar, and J. W. van Wingerden. "Recursive Nuclear Norm based Subspace Identification." IFAC-PapersOnLine 50, no. 1 (2017): 9490–95. http://dx.doi.org/10.1016/j.ifacol.2017.08.1585.

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43

Mattsson, Per, and Torbjörn Wigren. "Convergence analysis for recursive Hammerstein identification." Automatica 71 (September 2016): 179–86. http://dx.doi.org/10.1016/j.automatica.2016.04.014.

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44

Dumortier, F. A. G. "Theory and practice of recursive identification." Automatica 21, no. 4 (1985): 499–501. http://dx.doi.org/10.1016/0005-1098(85)90088-3.

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45

Xiao, Jianming, and Qijiang Song. "Recursive Identification of Quantized Linear Systems." Journal of Systems Science and Complexity 32, no. 4 (2019): 985–96. http://dx.doi.org/10.1007/s11424-019-8207-z.

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46

Bacchiocchi, Emanuele, Efrem Castelnuovo, and Luca Fanelli. "GIMME A BREAK! IDENTIFICATION AND ESTIMATION OF THE MACROECONOMIC EFFECTS OF MONETARY POLICY SHOCKS IN THE UNITED STATES." Macroeconomic Dynamics 22, no. 6 (2017): 1613–51. http://dx.doi.org/10.1017/s1365100516000833.

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We employ a non-recursive identification scheme to identify the effects of a monetary policy shock in a Structural Vector Autoregressive (SVAR) model for the US post-WWII quarterly data. The identification of the shock is achieved via heteroskedasticity, and different on-impact macroeconomic responses are allowed for (but not imposed) in each volatility regime. We show that the impulse responses obtained with the suggested non-recursive identification scheme are quite similar to those conditional on a recursive VAR estimated with pre-1984 data. In contrast, recursive vs. non-recursive identification schemes return different short-run responses of output and investment during the Great Moderation. Robustness checks dealing with a different definition of investment, an alternative break-point, and federal funds futures rates as an indicator of the monetary policy stance are documented and discussed.

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47

JAIN, SANJAY. "MINIMAL CONCEPT IDENTIFICATION AND RELIABILITY." International Journal of Foundations of Computer Science 09, no. 03 (1998): 315–20. http://dx.doi.org/10.1142/s0129054198000209.

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Identification, by algorithmic devices, of grammars for languages from positive data is a well studied problem. In this paper we are mainly concerned about the learnability of indexed families of uniformly recursive languages. Mukouchi introduced the notion of minimal and reliable minimal concept inference from positive data. He left open a question about whether every indexed family of uniformly recursive languages that is minimally inferable is also reliably minimally inferable. We show that this is not the case.

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48

Ben Mrad, R., and S. D. Fassois. "Recursive Identification of Vibrating Structures from Noise-Corrupted Observations, Part 1: Identification Approaches." Journal of Vibration and Acoustics 113, no. 3 (1991): 354–61. http://dx.doi.org/10.1115/1.2930192.

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In this paper the problem of recursive structural dynamics identification from noise-corrupted observations is addressed, and approaches that overcome the weaknesses of current methods, stemming from their underlying deterministic nature and ignorance of the fact that structural systems are inherently continuous-time, are introduced. Towards this end the problem is imbedded into a stochastic framework within which the inadequacy of standard Recursive Least Squares-based approaches is demonstrated. The fact that the continuous-time nature of structural systems necessitates the use of compatible triples of excitation signal type, model structure, and discrete-to-continuous transformation for modal parameter extraction is shown, and two such triples constructed. Based on these, as well as a new stochastic recursive estimation algorithm referred to as Recursive Filtered Least Squares (RFLS) and two other available schemes, a number of structural dynamics identification approaches are formulated and their performance characteristics evaluated. For this purpose structural systems with both well separated and closely spaced modes are used, and emphasis is placed on issues such as the achievable accuracy and resolution, rate of convergence, noise rejection, and computational complexity. The paper is divided into two parts: The problem formulation, the study of the interrelationships among excitation signal type, model structure, and discrete-to-continuous transformation, as well as the formulation of the stochastic identification approaches are presented in the first part, whereas a critical evaluation of their performance characteristics based on both simulated and experimental data is presented in the second.

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49

Finan, Daniel A., Francis J. Doyle, Cesar C. Palerm, et al. "Experimental Evaluation of a Recursive Model Identification Technique for Type 1 Diabetes." Journal of Diabetes Science and Technology 3, no. 5 (2009): 1192–202. http://dx.doi.org/10.1177/193229680900300526.

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Background: A model-based controller for an artificial β cell requires an accurate model of the glucose—insulin dynamics in type 1 diabetes subjects. To ensure the robustness of the controller for changing conditions (e.g., changes in insulin sensitivity due to illnesses, changes in exercise habits, or changes in stress levels), the model should be able to adapt to the new conditions by means of a recursive parameter estimation technique. Such an adaptive strategy will ensure that the most accurate model is used for the current conditions, and thus the most accurate model predictions are used in model-based control calculations. Methods: In a retrospective analysis, empirical dynamic autoregressive exogenous input (ARX) models were identified from glucose—insulin data for nine type 1 diabetes subjects in ambulatory conditions. Data sets consisted of continuous (5-minute) glucose concentration measurements obtained from a continuous glucose monitor, basal insulin infusion rates and times and amounts of insulin boluses obtained from the subjects' insulin pumps, and subject-reported estimates of the times and carbohydrate content of meals. Two identification techniques were investigated: nonrecursive, or batch methods, and recursive methods. Batch models were identified from a set of training data, whereas recursively identified models were updated at each sampling instant. Both types of models were used to make predictions of new test data. For the purpose of comparison, model predictions were compared to zero-order hold (ZOH) predictions, which were made by simply holding the current glucose value constant for p steps into the future, where p is the prediction horizon. Thus, the ZOH predictions are model free and provide a base case for the prediction metrics used to quantify the accuracy of the model predictions. In theory, recursive identification techniques are needed only when there are changing conditions in the subject that require model adaptation. Thus, the identification and validation techniques were performed with both “normal” data and data collected during conditions of reduced insulin sensitivity. The latter were achieved by having the subjects self-administer a medication, prednisone, for 3 consecutive days. The recursive models were allowed to adapt to this condition of reduced insulin sensitivity, while the batch models were only identified from normal data. Results: Data from nine type 1 diabetes subjects in ambulatory conditions were analyzed; six of these subjects also participated in the prednisone portion of the study. For normal test data, the batch ARX models produced 30-, 45-, and 60-minute-ahead predictions that had average root mean square error (RMSE) values of 26, 34, and 40 mg/dl, respectively. For test data characterized by reduced insulin sensitivity, the batch ARX models produced 30-, 60-, and 90-minute-ahead predictions with average RMSE values of 27, 46, and 59 mg/dl, respectively; the recursive ARX models demonstrated similar performance with corresponding values of 27, 45, and 61 mg/dl, respectively. The identified ARX models (batch and recursive) produced more accurate predictions than the model-free ZOH predictions, but only marginally. For test data characterized by reduced insulin sensitivity, RMSE values for the predictions of the batch ARX models were 9, 5, and 5% more accurate than the ZOH predictions for prediction horizons of 30, 60, and 90 minutes, respectively. In terms of RMSE values, the 30-, 60-, and 90-minute predictions of the recursive models were more accurate than the ZOH predictions, by 10, 5, and 2%, respectively. Conclusion: In this experimental study, the recursively identified ARX models resulted in predictions of test data that were similar, but not superior, to the batch models. Even for the test data characteristic of reduced insulin sensitivity, the batch and recursive models demonstrated similar prediction accuracy. The predictions of the identified ARX models were only marginally more accurate than the model-free ZOH predictions. Given the simplicity of the ARX models and the computational ease with which they are identified, however, even modest improvements may justify the use of these models in a model-based controller for an artificial β cell.

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50

Chen, Han-Fu. "A Unified Approach to Recursive System Identification." IFAC Proceedings Volumes 42, no. 10 (2009): 420–25. http://dx.doi.org/10.3182/20090706-3-fr-2004.00069.

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