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Journal articles on the topic 'Identification of an autoregressive model'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Sarichev, Aleksandr, and Bogdan Perviy. "AUTOREGRESSION MODELS OF SPACE OBJECTS MOVEMENT REPRESENTED BY TLE ELEMENTS." System technologies 2, no. 127 (February 24, 2020): 103–16. http://dx.doi.org/10.34185/1562-9945-2-127-2020-08.

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The developed method, which is a modification of the previously developed methods for constructing autoregressive models, is used to simulate the motion of space objects in the time series of their TLE elements. The modeling system has been developed that includes: determining the optimal volume of training samples in modeling time series of TLE elements; determination of the autoregression order for each variable (TLE element); determination of the optimal structure and identification of the parameters of the autoregressive model for each variable; identification of patterns of evolution of the mean square error of autoregressive models in time based on the modeling of time series of TLE elements according to the principle of "moving interval".

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2

Gilmour, Timothy P., Thyagarajan Subramanian, Constantino Lagoa, and W. Kenneth Jenkins. "Multiscale Autoregressive Identification of Neuroelectrophysiological Systems." Computational and Mathematical Methods in Medicine 2012 (2012): 1–5. http://dx.doi.org/10.1155/2012/580795.

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Electrical signals between connected neural nuclei are difficult to model because of the complexity and high number of paths within the brain. Simple parametric models are therefore often used. A multiscale version of the autoregressive with exogenous input (MS-ARX) model has recently been developed which allows selection of the optimal amount of filtering and decimation depending on the signal-to-noise ratio and degree of predictability. In this paper, we apply the MS-ARX model to cortical electroencephalograms and subthalamic local field potentials simultaneously recorded from anesthetized rodent brains. We demonstrate that the MS-ARX model produces better predictions than traditional ARX modeling. We also adapt the MS-ARX results to show differences in internuclei predictability between normal rats and rats with 6OHDA-induced parkinsonism, indicating that this method may have broad applicability to other neuroelectrophysiological studies.

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3

Goryainov, A. V., V. B. Goryainov, and W. M. Khing. "Robust Identification of an Exponential Autoregressive Model." Herald of the Bauman Moscow State Technical University. Series Natural Sciences, no. 4 (91) (August 2020): 42–57. http://dx.doi.org/10.18698/1812-3368-2020-4-42-57.

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One of the most common nonlinear time series (random processes with discrete time) models is the exponential autoregressive model. In particular, it describes such nonlinear effects as limit cycles, resonant jumps, and dependence of the oscillation frequency on amplitude. When identifying this model, the problem arises of estimating its parameters --- the coefficients of the corresponding autoregressive equation. The most common methods for estimating the parameters of an exponential model are the least squares method and the least absolute deviation method. Both of these methods have a number of disadvantages, to eliminate which the paper proposes an estimation method based on the robust Huber approach. The obtained estimates occupy an intermediate position between the least squares and least absolute deviation estimates. It is assumed that the stochastic sequence is described by the autoregressive equation of the first order, is stationary and ergodic, and the probability distribution of the innovations process of the model is unknown. Unbiased, consistency and asymptotic normality of the proposed estimate are established by computer simulation. Its asymptotic variance was found, which allows to obtain an explicit expression for the relative efficiency of the proposed estimate with respect to the least squares estimate and the least absolute deviation estimate and to calculate this efficiency for the most widespread probability distributions of the innovations sequence of the equation of the autoregressive model

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4

Ursu, Eugen, and Kamil Feridun Turkman. "Periodic autoregressive model identification using genetic algorithms." Journal of Time Series Analysis 33, no. 3 (January 19, 2012): 398–405. http://dx.doi.org/10.1111/j.1467-9892.2011.00772.x.

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5

Andrews, Beth, and Richard A. Davis. "Model identification for infinite variance autoregressive processes." Journal of Econometrics 172, no. 2 (February 2013): 222–34. http://dx.doi.org/10.1016/j.jeconom.2012.08.009.

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6

Liu, Jikui, Liyan Yin, Chenguang He, Bo Wen, Xi Hong, and Ye Li. "A Multiscale Autoregressive Model-Based Electrocardiogram Identification Method." IEEE Access 6 (2018): 18251–63. http://dx.doi.org/10.1109/access.2018.2820684.

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7

Ando, Shigeru. "Exact FFT-based identification of autoregressive (AR) model." Journal of the Acoustical Society of America 146, no. 4 (October 2019): 2846. http://dx.doi.org/10.1121/1.5136873.

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8

Liu, Xuan, and Jianbao Chen. "Variable Selection for the Spatial Autoregressive Model with Autoregressive Disturbances." Mathematics 9, no. 12 (June 21, 2021): 1448. http://dx.doi.org/10.3390/math9121448.

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Along with the rapid development of the geographic information system, high-dimensional spatial heterogeneous data has emerged bringing theoretical and computational challenges to statistical modeling and analysis. As a result, effective dimensionality reduction and spatial effect recognition has become very important. This paper focuses on variable selection in the spatial autoregressive model with autoregressive disturbances (SARAR) which contains a more comprehensive spatial effect. The variable selection procedure is presented by using the so-called penalized quasi-likelihood approach. Under suitable regular conditions, we obtain the rate of convergence and the asymptotic normality of the estimators. The theoretical results ensure that the proposed method can effectively identify spatial effects of dependent variables, find spatial heterogeneity in error terms, reduce the dimension, and estimate unknown parameters simultaneously. Based on step-by-step transformation, a feasible iterative algorithm is developed to realize spatial effect identification, variable selection, and parameter estimation. In the setting of finite samples, Monte Carlo studies and real data analysis demonstrate that the proposed penalized method performs well and is consistent with the theoretical results.

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9

Xiuli, Du, and Wang Fengquan. "Modal identification based on Gaussian continuous time autoregressive moving average model." Journal of Sound and Vibration 329, no. 20 (September 2010): 4294–312. http://dx.doi.org/10.1016/j.jsv.2010.04.018.

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10

Wu, Ping, ChunJie Yang, and ZhiHuan Song. "Recursive Subspace Model Identification Based On Vector Autoregressive Modelling." IFAC Proceedings Volumes 41, no. 2 (2008): 8872–77. http://dx.doi.org/10.3182/20080706-5-kr-1001.01499.

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11

Zhang, Yan, Guo Ying Zeng, Deng Feng Zhao, and Ming Yan Li. "Condition Identification of Bolted Joints Based on Autoregressive Model." Advanced Materials Research 433-440 (January 2012): 617–21. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.617.

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The difference signals, which between upper and lower shells of flange bolted joints structure, was applied to establish the autoregressive model for system condition identification. Firstly, an AR model is built by difference signals. The established AR model is used as a filter to process the difference signal in test state under the same condition and output residual series. Then the statistical parameters, such as Itakura distance, skewness, kurtosis and variance, are used to handle residual series. The results of experiment show that Itakura distance is a useful eigenvalue to identify the bolted joints condition.

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12

Yang, Jia-Hua, and Heung-Fai Lam. "An innovative Bayesian system identification method using autoregressive model." Mechanical Systems and Signal Processing 133 (November 2019): 106289. http://dx.doi.org/10.1016/j.ymssp.2019.106289.

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13

Johansen, Søren. "Some identification problems in the cointegrated vector autoregressive model." Journal of Econometrics 158, no. 2 (October 2010): 262–73. http://dx.doi.org/10.1016/j.jeconom.2010.01.007.

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14

Le Caillec, Jean-Marc. "Threshold autoregressive model blind identification based on array clustering." Signal Processing 184 (July 2021): 108055. http://dx.doi.org/10.1016/j.sigpro.2021.108055.

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15

Iqbal, Muhammad, and Amjad Naveed. "Forecasting Inflation: Autoregressive Integrated Moving Average Model." European Scientific Journal, ESJ 12, no. 1 (January 29, 2016): 83. http://dx.doi.org/10.19044/esj.2016.v12n1p83.

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This study compares the forecasting performance of various Autoregressive integrated moving average (ARIMA) models by using time series data. Primarily, The Box-Jenkins approach is considered here for forecasting. For empirical analysis, we used CPI as a proxy for inflation and employed quarterly data from 1970 to 2006 for Pakistan. The study classified two important models for forecasting out of many existing by taking into account various initial steps such as identification, the order of integration and test for comparison. However, later model 2 turn out to be a better model than model 1 after considering forecasted errors and the number of comparative statistics.

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16

Fukuda, T., and Y. Sunahara. "Parameter Identification of Fuzzy Autoregressive Models." IFAC Proceedings Volumes 26, no. 2 (July 1993): 689–92. http://dx.doi.org/10.1016/s1474-6670(17)48356-x.

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17

Shaarawy, Samir M., and Sherif S. Ali. "Bayesian Identification of Seasonal Autoregressive Models." Communications in Statistics - Theory and Methods 32, no. 5 (January 6, 2003): 1067–84. http://dx.doi.org/10.1081/sta-120019963.

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18

Li, Guibin. "Identification of fractional differencing autoregressive models†." Communications in Statistics - Theory and Methods 24, no. 10 (January 1995): 2635–43. http://dx.doi.org/10.1080/03610929508831638.

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19

Koreisha, Sergio G., and Tarmo Pukkila. "THE IDENTIFICATION OF SEASONAL AUTOREGRESSIVE MODELS." Journal of Time Series Analysis 16, no. 3 (May 1995): 267–90. http://dx.doi.org/10.1111/j.1467-9892.1995.tb00234.x.

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20

Zorzi, Mattia. "Autoregressive identification of Kronecker graphical models." Automatica 119 (September 2020): 109053. http://dx.doi.org/10.1016/j.automatica.2020.109053.

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21

Bauldry, Shawn, and Kenneth A. Bollen. "Nonlinear Autoregressive Latent Trajectory Models." Sociological Methodology 48, no. 1 (August 2018): 269–302. http://dx.doi.org/10.1177/0081175018789441.

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Autoregressive latent trajectory (ALT) models combine features of latent growth curve models and autoregressive models into a single modeling framework. The development of ALT models has focused primarily on models with linear growth components, but some social processes follow nonlinear trajectories. Although it is straightforward to extend ALT models to allow for some forms of nonlinear trajectories, the identification status of such models, approaches to comparing them with alternative models, and the interpretation of parameters have not been systematically assessed. In this paper we focus on two forms of nonlinear autoregressive latent trajectory (NLALT) models. The first form allows for a quadratic growth trajectory, a popular form of nonlinear latent growth curve models. The second form derives from latent basis models, or freed loading models, that allow for arbitrary growth processes. We discuss details concerning parameterization, model identification, estimation, and testing for the two forms of NLALT models. We include a simulation study that illustrates potential biases that may arise from fitting alternative models to data derived from an autoregressive process and individual-specific nonlinear trajectories. In addition, we include an extended empirical example modeling growth trajectories of weight from birth through age 2.

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22

NITTA, Masuhiro, Kenji SUGIMOTO, and Atsushi SATOH. "Blind System Identification of Autoregressive Model Using Independent Component Analysis." Transactions of the Society of Instrument and Control Engineers 41, no. 5 (2005): 444–51. http://dx.doi.org/10.9746/sicetr1965.41.444.

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23

SHI, C. S., and Y. MAO. "Elementary identification of a gnathosonic classification using an autoregressive model." Journal of Oral Rehabilitation 20, no. 4 (July 1993): 373–78. http://dx.doi.org/10.1111/j.1365-2842.1993.tb01620.x.

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24

UOSAKI, KATSUJI, and HIROSHI MORITA. "Discrete variable stochastic approximation procedures and recursive autoregressive model identification." International Journal of Systems Science 21, no. 10 (October 1990): 1951–63. http://dx.doi.org/10.1080/00207729008910516.

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25

Schmmid, W. "Identification of a Type I Outlier in an Autoregressive Model." Statistics 20, no. 4 (January 1989): 531–45. http://dx.doi.org/10.1080/02331888908802203.

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26

SUZUKI, Kohei, and Koh KAWANOBE. "Dynamic system identification based on two-dimensional autoregressive model fitting." Transactions of the Japan Society of Mechanical Engineers Series C 53, no. 487 (1987): 550–59. http://dx.doi.org/10.1299/kikaic.53.550.

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27

Chen, Yiding, and Xiaojin Zhu. "Optimal Attack against Autoregressive Models by Manipulating the Environment." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3545–52. http://dx.doi.org/10.1609/aaai.v34i04.5760.

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We describe an optimal adversarial attack formulation against autoregressive time series forecast using Linear Quadratic Regulator (LQR). In this threat model, the environment evolves according to a dynamical system; an autoregressive model observes the current environment state and predicts its future values; an attacker has the ability to modify the environment state in order to manipulate future autoregressive forecasts. The attacker's goal is to force autoregressive forecasts into tracking a target trajectory while minimizing its attack expenditure. In the white-box setting where the attacker knows the environment and forecast models, we present the optimal attack using LQR for linear models, and Model Predictive Control (MPC) for nonlinear models. In the black-box setting, we combine system identification and MPC. Experiments demonstrate the effectiveness of our attacks.

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28

Lee, Lung-fei, and Xiaodong Liu. "EFFICIENT GMM ESTIMATION OF HIGH ORDER SPATIAL AUTOREGRESSIVE MODELS WITH AUTOREGRESSIVE DISTURBANCES." Econometric Theory 26, no. 1 (August 13, 2009): 187–230. http://dx.doi.org/10.1017/s0266466609090653.

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In this paper, we extend the GMM framework for the estimation of the mixed-regressive spatial autoregressive model by Lee(2007a) to estimate a high order mixed-regressive spatial autoregressive model with spatial autoregressive disturbances. Identification of such a general model is considered. The GMM approach has computational advantage over the conventional ML method. The proposed GMM estimators are shown to be consistent and asymptotically normal. The best GMM estimator is derived, within the class of GMM estimators based on linear and quadratic moment conditions of the disturbances. The best GMM estimator is asymptotically as efficient as the ML estimator under normality, more efficient than the QML estimator otherwise, and is efficient relative to the G2SLS estimator.

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29

Kim, T. R., K. F. Ehmann, and S. M. Wu. "Identification of Joint Structural Parameters Between Substructures." Journal of Engineering for Industry 113, no. 4 (November 1, 1991): 419–24. http://dx.doi.org/10.1115/1.2899716.

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A new methodology of combining the finite element model of a complex structure with its model obtained by experimental modal analysis techniques is presented to identify the joint stiffness and the damping characteristics between its substructures. First, the modal parameters of the structure with joints are extracted based on experimental data using Autoregressive Moving Average Vector models. Then, a condensation technique based on the Riccati iteration algorithm and the orthogonality conditions is applied to reduce the matrix order of the finite element model to match the order of the experimental model. Comparing the two models, the unknown joint parameters are estimated based on the least squares method. The accuracy and the effectiveness of the proposed method were verified through simulation studies.

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30

Brandt, Patrick T., and John T. Williams. "A Linear Poisson Autoregressive Model: The Poisson AR(p) Model." Political Analysis 9, no. 2 (2001): 164–84. http://dx.doi.org/10.1093/oxfordjournals.pan.a004869.

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Time series of event counts are common in political science and other social science applications. Presently, there are few satisfactory methods for identifying the dynamics in such data and accounting for the dynamic processes in event counts regression. We address this issue by building on earlier work for persistent event counts in the Poisson exponentially weighted moving-average model (PEWMA) of Brandt et al. (American Journal of Political Science44(4):823–843, 2000). We develop an alternative model for stationary mean reverting data, the Poisson autoregressive model of orderp, or PAR(p) model. Issues of identification and model selection are also considered. We then evaluate the properties of this model and present both Monte Carlo evidence and applications to illustrate.

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31

Le Caillec, Jean-Marc, and René Garello. "Nonlinear system identification using autoregressive quadratic models." Signal Processing 81, no. 2 (February 2001): 357–79. http://dx.doi.org/10.1016/s0165-1684(00)00213-9.

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32

Huang, Jianhua Z., and Lijian Yang. "Identification of non-linear additive autoregressive models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 66, no. 2 (May 2004): 463–77. http://dx.doi.org/10.1111/j.1369-7412.2004.05500.x.

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33

Masarotto, G. "Robust Identification of Autoregressive Moving Average Models." Applied Statistics 36, no. 2 (1987): 214. http://dx.doi.org/10.2307/2347553.

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34

Delyon, Bernard, and Anatoli Juditsky. "On minimax identification of nonparametric autoregressive models." Probability Theory and Related Fields 116, no. 1 (January 2000): 21–39. http://dx.doi.org/10.1007/pl00008721.

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35

HUNG, C. F., and W. J. KO. "IDENTIFICATION OF MODAL PARAMETERS FROM MEASURED OUTPUT DATA USING VECTOR BACKWARD AUTOREGRESSIVE MODEL." Journal of Sound and Vibration 256, no. 2 (September 2002): 249–70. http://dx.doi.org/10.1006/jsvi.2001.4205.

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36

Piltan, Farzin, Bach Phi Duong, and Jong-Myon Kim. "Deep Learning-Based Adaptive Neural-Fuzzy Structure Scheme for Bearing Fault Pattern Recognition and Crack Size Identification." Sensors 21, no. 6 (March 17, 2021): 2102. http://dx.doi.org/10.3390/s21062102.

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Bearings are complex components with onlinear behavior that are used to mitigate the effects of inertia. These components are used in various systems, including motors. Data analysis and condition monitoring of the systems are important methods for bearing fault diagnosis. Therefore, a deep learning-based adaptive neural-fuzzy structure technique via a support vector autoregressive-Laguerre model is presented in this study. The proposed scheme has three main steps. First, the support vector autoregressive-Laguerre is introduced to approximate the vibration signal under normal conditions and extract the state-space equation. After signal modeling, an adaptive neural-fuzzy structure observer is designed using a combination of high-order variable structure techniques, the support vector autoregressive-Laguerre model, and adaptive neural-fuzzy inference mechanism for normal and abnormal signal estimation. The adaptive neural-fuzzy structure observer is the main part of this work because, based on the difference between signal estimation accuracy, it can be used to identify faults in the bearings. Next, the residual signals are generated, and the signal conditions are detected and identified using a convolution neural network (CNN) algorithm. The effectiveness of the proposed deep learning-based adaptive neural-fuzzy structure technique by support vector autoregressive-Laguerre model was analyzed using the Case Western Reverse University (CWRU) bearing vibration dataset. The proposed scheme is compared to five state-of-the-art techniques. The proposed algorithm improved the average pattern recognition and crack size identification accuracy by 1.99%, 3.84%, 15.75%, 5.87%, 30.14%, and 35.29% compared to the combination of the high-order variable structure technique with the support vector autoregressive-Laguerre model and CNN, the combination of the variable structure technique with the support vector autoregressive-Laguerre model and CNN, the combination of RAW signal and CNN, the combination of the adaptive neural-fuzzy structure technique with the support vector autoregressive-Laguerre model and support vector machine (SVM), the combination of the high-order variable structure technique with the support vector autoregressive-Laguerre model and SVM, and the combination of the variable structure technique with the support vector autoregressive-Laguerre model and SVM, respectively.

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37

Pan, Jian, Hao Ma, Xiao Jiang, Wenfang Ding, and Feng Ding. "Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique." Complexity 2018 (July 24, 2018): 1–11. http://dx.doi.org/10.1155/2018/9598307.

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The identification problem of multivariable controlled autoregressive systems with measurement noise in the form of the moving average process is considered in this paper. The key is to filter the input–output data using the data filtering technique and to decompose the identification model into two subidentification models. By using the negative gradient search, an adaptive data filtering-based gradient iterative (F-GI) algorithm and an F-GI with finite measurement data are proposed for identifying the parameters of multivariable controlled autoregressive moving average systems. In the numerical example, we illustrate the effectiveness of the proposed identification methods.

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38

Hasan, Md K., A. K. M. Z. R. Chowdhury, and M. R. Khan. "Identification of autoregressive signals in colored noise using damped sinusoidal model." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 50, no. 7 (July 2003): 966–69. http://dx.doi.org/10.1109/tcsi.2003.813954.

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39

Makinde, Olusola Samuel, and Olusoga Akin Fasoranbaku. "Identification of Optimal Autoregressive Integrated Moving Average Model on Temperature Data." Journal of Modern Applied Statistical Methods 10, no. 2 (November 1, 2011): 718–29. http://dx.doi.org/10.22237/jmasm/1320121800.

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40

Zamyad, Hojat, Nadia Naghavi, Reza Godaz, and Reza Monsefi. "A recurrent neural network–based model for predicting bending behavior of ionic polymer–metal composite actuators." Journal of Intelligent Material Systems and Structures 31, no. 17 (July 21, 2020): 1973–85. http://dx.doi.org/10.1177/1045389x20942318.

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The high application potential of ionic polymer–metal composites has made the behavior identification of this group of smart materials an attractive area. So far, several models have been proposed to predict the bending of an ionic polymer–metal composite actuator, but these models have some weaknesses, the most important of them are the use of output data (in autoregressive models), high complexity to achieve a proper precision (in non-autoregressive models), and lack of compatibility with the behavioral nature of the material. In this article, we present a hybrid model of parallel non-autoregressive recurrent networks with internal memory cells to overcome existing weaknesses. The validation results on experimental data show that the proposed model has acceptable accuracy and flexibility. Moreover, simplicity and compatibility with the behavioral nature of the material promote using the proposed model in practical applications.

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41

Dernlugkam, Narongkorn, and Parkpoom Chokchairungroj. "System Identification of a Prototype of Hydraulic Platform Using ARX Model and Lagrange’s Equation." Applied Mechanics and Materials 704 (December 2014): 341–44. http://dx.doi.org/10.4028/www.scientific.net/amm.704.341.

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This paper presents the system identification of a prototype of hydraulic platform compare with two methods between the Autoregressive model structure with exogenous input and the Lagrange’s equation with SolidWorks’ simulation analysis tool. The system identification was identified, then the model was employed to simulate the multi-sine input response and the simulation results were compared to the experimental results. It was found that the model was developed using Lagrange’s equation more accurate than the Autoregressive model structure that having percentage best fit of 93.45% for the azimuth angle control and 92.25% for the elevation angle control. Overall, this method could be accepted with a good percentage best fit that compared with experiment results.

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42

Stefanoiu, Dan, and Janetta Culita. "Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data." Sensors 20, no. 18 (September 4, 2020): 5038. http://dx.doi.org/10.3390/s20185038.

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This article introduces the spline approximation concept, in the context of system identification, aiming to obtain useful autoregressive models of reduced order. Models with a small number of poles are extremely useful in real time control applications, since the corresponding regulators are easier to design and implement. The main goal here is to compare the identification models complexity when using two types of experimental data: raw (affected by noises mainly produced by sensors) and smoothed. The smoothing of raw data is performed through a least squares optimal stochastic cubic spline model. The consecutive data points necessary to build each polynomial of spline model are adaptively selected, depending on the raw data behavior. In order to estimate the best identification model (of ARMAX class), two optimization strategies are considered: a two-step one (which provides first an optimal useful model and then an optimal noise model) and a global one (which builds the optimal useful and noise models at once). The criteria to optimize rely on the signal-to-noise ratio, estimated both for identification and validation data. Since the optimization criteria usually are irregular in nature, a metaheuristic (namely the advanced hill climbing algorithm) is employed to search for the model optimal structure. The case study described in the end of the article is concerned with a real plant with nonlinear behavior, which provides noisy acquired data. The simulation results prove that, when using smoothed data, the optimal useful models have significantly less poles than when using raw data, which justifies building cubic spline approximation models prior to autoregressive identification.

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43

Shi, Zhaoyun, and Hisayuki Aoyama. "Identification of the Self-Organizing Exponential Autoregressive Models." IFAC Proceedings Volumes 30, no. 11 (July 1997): 1321–25. http://dx.doi.org/10.1016/s1474-6670(17)43025-4.

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44

Hall, Alastair. "ORDER IDENTIFICATION IN MISSPECIFIED AUTOREGRESSIVE TIME SERIES MODELS." Journal of Time Series Analysis 15, no. 3 (May 1994): 279–83. http://dx.doi.org/10.1111/j.1467-9892.1994.tb00193.x.

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45

Nozari, Erfan, Yingbo Zhao, and Jorge Cortes. "Network Identification With Latent Nodes via Autoregressive Models." IEEE Transactions on Control of Network Systems 5, no. 2 (June 2018): 722–36. http://dx.doi.org/10.1109/tcns.2017.2754372.

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46

HAYASHI, Koji, Yoshikuni SHINOHARA, and Hidetoshi KONNO. "Identification of Nonlinear Autoregressive Model Based on GMDH and Its Spectral Analysis." Transactions of the Society of Instrument and Control Engineers 28, no. 10 (1992): 1216–23. http://dx.doi.org/10.9746/sicetr1965.28.1216.

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47

He, Xia, and G. De Roeck. "System identification of mechanical structures by a high-order multivariate autoregressive model." Computers & Structures 64, no. 1-4 (July 1997): 341–51. http://dx.doi.org/10.1016/s0045-7949(96)00126-5.

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48

Fattah, S. A., W. P. Zhu, and M. O. Ahmad. "Identification of Autoregressive Systems in Noise Based on a Ramp-Cepstrum Model." IEEE Transactions on Circuits and Systems II: Express Briefs 55, no. 10 (October 2008): 1051–55. http://dx.doi.org/10.1109/tcsii.2008.925660.

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49

Uosaki, Katsuji, and Toshiharu Hatanaka. "MULTI-OBJECTIVE OPTIMIZATION APPROACH TO OPTIMAL INPUT DESIGN FOR AUTOREGRESSIVE MODEL IDENTIFICATION." IFAC Proceedings Volumes 38, no. 1 (2005): 476–81. http://dx.doi.org/10.3182/20050703-6-cz-1902.00080.

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Ando, Shigeru. "Frequency-Domain Prony Method for Autoregressive Model Identification and Sinusoidal Parameter Estimation." IEEE Transactions on Signal Processing 68 (2020): 3461–70. http://dx.doi.org/10.1109/tsp.2020.2998929.

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