Approximate stochastic realization and robust prediction: algorithms for iterative solution

Research output: Contribution to journalConference article

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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 languageEnglish (US)
Pages (from-to)738-744
Number of pages7
JournalProceedings of the IEEE Conference on Decision and Control
Volume1
StatePublished - Dec 1 1994
EventProceedings of the 33rd IEEE Conference on Decision and Control. Part 1 (of 4) - Lake Buena Vista, FL, USA
Duration: Dec 14 1994Dec 16 1994

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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