Abstract
This paper presents the new identification procedure of the discrete-time stochastic model in linear time-variant system. The model to be identified has the random input with the white Gaussian property. The variance of the white Gaussian input and the autocovariance function of the signal are assumed to be known. The linear time-variant system is identified by these covariance information.
Keywords
- Identification Algorithm
- Covariance Information
- State Transition Matrix
- Autocovariance Function
- Kalman Filter Equation
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© 1988 International Federation for Information Processing
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Nakamori, S., Tokumaru, H. (1988). Identification of linear discrete-time stochastic system using covariance information. In: Iri, M., Yajima, K. (eds) System Modelling and Optimization. Lecture Notes in Control and Information Sciences, vol 113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0042779
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DOI: https://doi.org/10.1007/BFb0042779
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-19238-1
Online ISBN: 978-3-540-39164-7
eBook Packages: Springer Book Archive