Multidimensional Systems and Signal Processing
Modeling and identification of LPTV systems by wavelets
Signal Processing
Wavelet-based MPNLMS adaptive algorithm for network echo cancellation
EURASIP Journal on Audio, Speech, and Music Processing
Identification of a linear time-varying system using Haar wavelet
SIP'08 Proceedings of the 7th WSEAS International Conference on Signal Processing
Identification of a continuous linear time-varying system using Haar wavelet with unit energy
WSEAS Transactions on Circuits and Systems
Non-parametric linear time-invariant system identification by discrete wavelet transforms
Digital Signal Processing
Continuous wavelet based linear time-varying system identification
Signal Processing
Technical Communique: Wavelet-Based Identification of Linear Discrete-Time Systems: Robustness Issue
Automatica (Journal of IFAC)
Adaptive wavelet predictor to improve bandwidth allocation efficiency of VBR video traffic
Computer Communications
Hi-index | 35.69 |
It is shown how linear time-varying systems can be modeled in several different ways by discrete-time wavelets or, more generally, by some set of functions. Interpretation of physical meanings, possible efficiency, and other characteristics of the modeling are considered. System identification minimizing the mean square output error is studied. Optimal coefficients and the corresponding minimum mean square error are found, and they are, in general, time varying. Least-mean-square adaptive filtering algorithms are derived for on-line filtering and system Identification. Theoretically and by simulations, the advantages of using wavelet-based filtering are shown: separation of adaptation effects from unknown time-varying system behavior and fast convergence. Adaptive coefficients estimated by a recursive-least-square algorithm can tend toward constants, even in the case of time-varying systems. Time-invariant system identification and adaptive filtering is given as a special case of the general time-varying setting