Research Article
A Wavelet Approach for Identification of Linear Time Invariant System
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 50 - Issue 20 |
| Published: July 2012 |
| Authors: Ramesh Kumar, Chitranjan Kumar, Manoj Kumar |
10.5120/7918-1213
|
Ramesh Kumar, Chitranjan Kumar, Manoj Kumar . A Wavelet Approach for Identification of Linear Time Invariant System. International Journal of Computer Applications. 50, 20 (July 2012), 13-16. DOI=10.5120/7918-1213
@article{ 10.5120/7918-1213,
author = { Ramesh Kumar,Chitranjan Kumar,Manoj Kumar },
title = { A Wavelet Approach for Identification of Linear Time Invariant System },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 50 },
number = { 20 },
pages = { 13-16 },
doi = { 10.5120/7918-1213 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Ramesh Kumar
%A Chitranjan Kumar
%A Manoj Kumar
%T A Wavelet Approach for Identification of Linear Time Invariant System%T
%J International Journal of Computer Applications
%V 50
%N 20
%P 13-16
%R 10.5120/7918-1213
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Wavelet transformation has been applied to various problems of system identification. In this paper, a wavelet based approach for the identification of time-invariant system is proposed. In this approach, mother wavelet is used for excitation to find the impulse response, which can be estimated at half the available number of points of the sampled output sequence. This method has been compared with some other standard techniques such as frequency chirp, coherence function and inverse filtering. In chirp method, wideband excitation such as frequency chirp is used. Frequency response is obtained as the DFT of the output of the system for time-domain input. Inverse method uses SVD function to find pseudoinverse. Coherence function has been used to identify the system using MATLAB function tfestimate. The performances of the methods are demonstrated by means of experimental investigation.
References
- D. J. Ewins. Modal: Theory, Practice and applications. Engineering Dynamic Series. Research Studies press Ltd. , Baldock, Hertfordshire, England, second edition,2000.
- D. J. Ewins and D. J. Inmam, editors. Structural Dynamics 2000: current direction and future detection. Engineering Dynamic Series. Research Studies press Ltd. , Baldock, Hertfordshire, England,2001
- A. Juditsky, Q. Zhang, B. Delyon, P. -Y. Glorennec, A. Benveniste, Wavelets in identification, IRISA Publication No. 849, September 1994.
- M. Pawlak, Z. Hasiewicz, Nonlinear system identification by the Haar multiresolution analysis, IEEE Trans. Circuits Systems I–Fundam. Theory Appl. 45 (9) (1998) 945–961.
- W. J. Staszewski, Identification of non-linear systems using multi-scale ridges and skeletons of the wavelet transform, J. Sound Vibrat. 214 (4) (1998) 639–658.
- W. J. Staszewski, Analysis of non-linear systems using wavelets, Proc. Inst. Mech. Eng. C 214 (11) (2000) 1339– 1353.
- D. Coca, S. A. Billings, Non-linear system identification using wavelet multiresolution models, Int. J. Control 74 (18) (2001) 1718–1736.
- R. Ghanem, F. Romeo, A wavelet-based approach for model and parameter identification of non-linear systems, Int. J. Nonlinear Mech. 36 (5) (2001) 835–859.
- M. Pawlak, Z. Hasiewicz, Nonlinear system identification by the Haar multiresolution analysis, IEEE Trans. Circuits Systems I–Fundam. Theory Appl. 45 (9) (1998) 945–961.
- A. N. Robertson, K. C. Park, K. F. Alvin, Extraction of impulse response data via wavelet transform for structural system identification, J. Vibrat. Acoust. Trans. ASME-120 (1) (1998) 252–260.
- M. K. Tsatanis, G. B. Giannakis, Time-varying system identification and model validation using wavelets, IEEE Trans. Signal Process. 41 (12) (1993) 3512–3523.
- M. I. Doroslova?cki, H. Fan, L. Yao,Wavelet-based identification of linear discrete-time systems: Robustness issues, Automatica 34 (12) (1998) 1637–1640.
- Y. Zheng, Z. Lin, D. B. H. Tay, Time-varying parametric system multiresolution identification by wavelets, Int. J. Syst. Sci. 32 (6) (2001)
- H. Guo, C. S. Burrus, Wavelet and image compression using the Burrows Wheeler transform and the wavelet transform, in: Proceedings of the IEEE International Conference on Image Processing, ICIP-97, vol. I, Santa Barbara, CA, October 1997, pp. 65–68.
- D. L. Donoho, De-noising by soft-thresholding, IEEE Trans. Inform. Theory 41 (3) (1995) 613–627.
- S. Del Marco, J. Weiss, Improved transient signal detection using a wave packet-based detector with an extended translation-invariant wavelet transform, IEEE Trans. Signal Process. 45 (4) (1997) 841–850.
- N. Erdol, F. Basbug,Wavelet transform based adaptive filtering, in: J. Vandewalle, R. Boite, M. Moone, A. Oosterlinck (Eds. ), Signal Processing VI: Theories and Applications, Elsevier, Amsterdam, 1992, pp. 1117–1120.
- S. Hosur, A. H. Tewik,Wavelet transform domain LMS algorithm, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 3, Minneapolis, MN, April 1993, pp. 508–510. 775– 793.
- C. S. Burrus, R. A. Gopinath, H. Guo, Introduction to Wavelets and Wavelet Transforms: A Primer, Prentice Hall, Upper Saddle River, NJ, 1997.
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