Finite-Data Algorithms for Approximate Stochastic Realization

  • Richard J. Vaccaro


This chapter deals with the problem of constructing a state-space model for a stochastic process from a finite number of estimated covariance lags. The approach is to first obtain a high-order model which exactly matches the estimated covariance sequence, and then use balanced model reduction techniques to obtain a lower order model which approximates the given sequence. It is shown that balanced models can be obtained from a realization algorithm which uses an infinite covariance sequence. Scaling ideas are then introduced so that balanced realizations can be obtained from finite covariance sequences.


Singular Value Decomposition Innovation Model Riccati Equation Covariance Sequence Model Reduction 
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Copyright information

© Kluwer Academic Publishers, Boston 1986

Authors and Affiliations

  • Richard J. Vaccaro
    • 1
  1. 1.Department of Electrical EngineeringUniversity of Rhode IslandKingstonUSA

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