A Wavelet Approach for Identification of Linear Time Invariant System

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.

[1]  Georgios B. Giannakis,et al.  Time-varying system identification and model validation using wavelets , 1993, IEEE Trans. Signal Process..

[2]  David B. H. Tay,et al.  Time-varying parametric system multiresolution identification by wavelets , 2001, Int. J. Syst. Sci..

[3]  K. Park,et al.  Extraction of Impulse Response Data via Wavelet Transform for Structural System Identification , 1998 .

[4]  Milos Doroslovacki,et al.  Wavelet-Based Identification of Linear Discrete-Time Systems: Robustness Issue , 1998, Autom..

[5]  W J Staszewski,et al.  Analysis of non-linear systems using wavelets , 2000 .

[6]  C. Sidney Burrus,et al.  Waveform and image compression using the Burrows Wheeler transform and the wavelet transform , 1997, Proceedings of International Conference on Image Processing.

[7]  Stephen Del Marco,et al.  Improved transient signal detection using a wavepacket-based detector with an extended translation-invariant wavelet transform , 1997, IEEE Trans. Signal Process..

[8]  Zygmunt Hasiewicz,et al.  Nonlinear system identification by the Haar multiresolution analysis , 1998 .

[9]  Bernard Delyon,et al.  Wavelets in identification , 1994, Fuzzy logic and expert systems applications.

[10]  Srinath Hosur,et al.  Wavelet transform domain LMS algorithm , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[12]  W. Staszewski IDENTIFICATION OF NON-LINEAR SYSTEMS USING MULTI-SCALE RIDGES AND SKELETONS OF THE WAVELET TRANSFORM , 1998 .

[13]  Daniel Coca,et al.  Non-linear system identification using wavelet multiresolution models , 2001 .

[14]  Søren Holdt Jensen,et al.  Signal Processing VI: Theories and Applications , 1992 .

[15]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[16]  Milos Doroslovacki,et al.  Wavelet-Based Identification of Linear Discrete-Time Systems , 1997 .

[17]  Marjorie V. Batey,et al.  AUTHORS. IN PROFILE , 1969 .

[18]  R. Ghanem,et al.  A wavelet-based approach for model and parameter identification of non-linear systems , 2001 .