Paper Info
Title
Maximum likelihood identification of multiscale stochastic models using the wavelet transform and the EM algorithm
Abstract
The authors address the problem of estimating the parameters of a class of multiscale stochastic processes that can be modeled by state-space dynamic systems driven by white noise in scale rather than in time. They present a maximum likelihood identification method for estimating the parameters of the multiscale stochastic models given data which are based on the wavelet transform and the expectation-maximization algorithm. Numerical examples are provided for identifying the parameters of the state-space models based on synthesized data to demonstrate the accuracy and the efficiency of the algorithm. In the examples the effects of performing system identification are illustrated based on data at both multiple and single scales. The single-scale case can be viewed as the standard problem of fitting model parameters to data, where here the model is not standard.<>
Year
DOI
Venue
1993
10.1109/ICASSP.1993.319602
ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
Keywords
Field
DocType
maximum likelihood estimation,parameter estimation,signal processing,state-space methods,stochastic processes,wavelet transforms,white noise,EM algorithm,accuracy,efficiency,expectation-maximization algorithm,maximum likelihood identification,multiscale stochastic processes,state-space dynamic systems,system identification,wavelet transform,white noise
Mathematical optimization,Pattern recognition,Computer science,Expectation–maximization algorithm,Image processing,Stochastic process,White noise,Stochastic modelling,Artificial intelligence,Estimation theory,System identification,Wavelet transform
Conference
Volume
ISSN
ISBN
4
1520-6149
0-7803-0946-4
Citations 
PageRank 
References 
5
1.25
4
Authors
2
Name
Order
Citations
PageRank
Digalakis, V.V.1979.13
Chou, K.C.251.25