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Using wavelet in identification state space models | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 210، دوره 13، شماره 1، خرداد 2022، صفحه 2573-2578 اصل مقاله (405.49 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.5958 | ||
| نویسنده | ||
| Heyam A. A. Hayawi* | ||
| Department of Statistics and Informatics, University of Mosul, Mosul, Iraq | ||
| تاریخ دریافت: 21 شهریور 1400، تاریخ بازنگری: 23 مهر 1400، تاریخ پذیرش: 16 آذر 1400 | ||
| چکیده | ||
| In this research, an attempt to address a problem that is still intractable for researchers in the field of diagnosing dynamic systems, including state space models, which are related to determining the type of process appropriate to the nature of the system and determining its rank. Accordingly, the wavelet method will be adopted in the diagnostic process and the parameters of these models will be estimated using the least-squares method, applying this to real data and comparing the results based on a set of statistical and engineering criteria using the ready-made program Matlab. | ||
| کلیدواژه‌ها | ||
| Time Series؛ State Space؛ Identification؛ wavelet | ||
| مراجع | ||
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[1] P.S. Addison, The Illustrated Wavelet Transform Handbook, Institute of Physics Publishing, Bristol, UK, 2002. [2] G. Box, G. Jenkins, G. Reinseln and G. Ljung, Time Series Analysis Forecasting and Control, John wiley & Sons, Inc., Hoboken, New Jersey, 2016. [3] C. Jose, G. Alfredo, J. Miguel, S. Sonia and T. Alexandre, State Space Methods for Time Series Analysis Theory, Applications and Software, CRC Press Taylor & France Group,A CHAPMAN & HALL Book, 2016. [4] R.E. Kalman and J. Bertram,Control system analysis and design via the second method of Lyapunov, J. Basic Engin. Trans. ASME 82 (1960) 371–400. [5] P.P. Kanjilal, Adaptive Prediction and Predictive Control, Peter Peregrines Ltd. London, 1995. [6] S. Makridakis, S. WheelWright and R. Hydman, Forecasting: Methods and Applications, John-Wiley and Sons,New York, 1998. [7] S.A. Mallat, Wavlet Tour of Signal Processing, Academic Press, 1997. [8] O. Nelles,Nonlinear System Identification From Classical Approach to Neural Network and Fuzzy Models, Springer Verlag Berlin, Heidelberg, Germany, 2001. [9] B. Sidney, R.A. Gopinath and G. Haitao, Introduction to Wavelets and Wavelet Transforms, Prentice-Hall Onc., New York, 1998. [10] J.S. Walker,A primer on Wavelet and Their Scientific Application, PP.305, Tayler and Francis Group, LLC., 2008. [11] C. Wogoutong, Imputation methods in time series with a trend and a consecutive missing value pattern, Thai. Stat. J. 19(4) (2021) 866–879. [12] E. Zandonade and P.A. Morettin, Wavelet in state space models, J. Appl. Stochastic Model. Business Indust. 19(3) (2003) 199–219. | ||
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