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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 738-744 |
| Number of pages | 7 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 1 |
| State | Published - 1994 |
| Event | Proceedings of the 33rd IEEE Conference on Decision and Control. Part 1 (of 4) - Lake Buena Vista, FL, USA Duration: Dec 14 1994 → Dec 16 1994 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization