Title :
Approximate stochastic realization and robust prediction: algorithms for iterative solution
Author :
Poor, H. Vincent
Author_Institution :
Dept. of Electr. Eng., Princeton Univ., NJ, USA
Abstract :
The related problems of (finite-length) robust prediction and maximum-entropy approximate stochastic realization are considered. Such problems are of interest in situations where there is uncertainty in the finite-length covariance data of an observed signal or time series. General properties of iterative solutions of these problems are developed, and two iterative algorithms that converge monotonically to such solutions are presented for the situation in which the uncertainty class is a simplex
Keywords :
approximation theory; covariance matrices; iterative methods; maximum entropy methods; prediction theory; realisation theory; time series; finite-length covariance data; iterative solution; maximum-entropy approximate stochastic realization; observed signal; robust prediction; time series; uncertainty class; Centralized control; Data compression; Entropy; Iterative algorithms; Minimax techniques; Prediction algorithms; Predictive models; Robustness; Stochastic processes; Uncertainty;
Conference_Titel :
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location :
Lake Buena Vista, FL
Print_ISBN :
0-7803-1968-0
DOI :
10.1109/CDC.1994.410865
