Title :
Behaviour analysis of adaptive ARMA predictors with nonstationary inputs
Author :
M´Sirdi, N. ; Macchi, O. ; Zarader, J.
Author_Institution :
Lab. de Robotique, Univ. Pierre et Marie Curie, Paris, France
Abstract :
The behavior of autoregressive moving average (ARMA) predictors is analyzed for neglected dynamic cases with nonstationary signals. For this analysis, nonstationarity types must be considered (abrupt or smooth changes in the mean and variance). Some procedures can be used to improve the algorithm performances and limit the self-stabilization oscillations. The resulting algorithm is not much more complex than the NLMS algorithm and can be used for applications like ADPCM or data compression with highly nonstationary signals
Keywords :
adaptive systems; data compression; filtering and prediction theory; pulse-code modulation; ADPCM; adaptive ARMA predictors; algorithm performances; autoregressive moving average; data compression; dynamic cases; nonstationary inputs; nonstationary signals; self-stabilization oscillations; Algorithm design and analysis; Analysis of variance; Data compression; Error correction; Estimation error; Least squares approximation; Narrowband; Predictive models; Pulse modulation; Robots; Signal analysis; Signal processing algorithms;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on
Conference_Location :
Albuquerque, NM
DOI :
10.1109/ICASSP.1990.115845
