Abstract
This chapter presents the essential elements of the minimum variance filtering and prediction algorithms derived on the basis of the state space and the CARMA models. After a brief review of the two forms of stochastic signal models, the estimation algorithms are first derived for systems with known models. This is followed by the introduction of a sequential parameter estimation algorithm for systems with unknown models. A solution of the adaptive filtering and prediction problem for systems with unknown models is then discussed. Finally, a numerical example illustrating the application of the adaptive prediction algorithm for solving a simple control problem is also presented.
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© 1990 Kluwer Academic Publishers
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Mahalanabis, A.K. (1990). Estimation and Signal Processing Algorithms. In: Tzafestas, S.G., Pal, J.K. (eds) Real Time Microcomputer Control of Industrial Processes. Microprocessor-Based Systems Engineering, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0609-9_4
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DOI: https://doi.org/10.1007/978-94-009-0609-9_4
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6761-4
Online ISBN: 978-94-009-0609-9
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