Abstract
The paper présentes a numerically stable and general algorithm for identification and realization of a complete dynamic linear state space model, including the system order, for combined deterministic and stochastic systems from time series. A special property of this algorithm is that the innovations covariance matrix and the Markov parameters for the stochastic sub-system are determined directly from a projection of known data matrices, without e.g. recursions of non-linear matrix Riccatti equations. A realization of the Kaiman filter gain matrix is determined from the estimated extended observability matrix and the Markov parameters. Monte Carlo simulations are used to analyze the statistical properties of the algorithm as well as to compare with existing algorithms.
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© 1997 Springer-Verlag New York, Inc.
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Di Ruscio, D. (1997). A Method for Identification of Combined Deterministic Stochastic Systems. In: Aoki, M., Havenner, A.M. (eds) Applications of Computer Aided Time Series Modeling. Lecture Notes in Statistics, vol 119. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2252-1_8
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DOI: https://doi.org/10.1007/978-1-4612-2252-1_8
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