Abstract
Fractional representation by polynomial matrices is a tool for describing linear dynamical systems, often providing a unique insight into the systems. It has turned out that the coefficient matrices in this representation can be identified without knowing the input data, under some statistic assumptions. This is an outcome by combining system theory with a recent progress in signal processing; i.e., a methodology generically called independent component analysis.
This article summarizes this technique of blind system identification. Restricted optimization for parameter estimation plays a key role. Also presented are some of its applications in various fields such as input distortion compensation, disturbance suppression, and time series prediction in financial engineering.
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Sugimoto, K. (2010). Blind Identification of Polynomial Matrix Fraction with Applications. In: Willems, J.C., Hara, S., Ohta, Y., Fujioka, H. (eds) Perspectives in Mathematical System Theory, Control, and Signal Processing. Lecture Notes in Control and Information Sciences, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93918-4_34
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DOI: https://doi.org/10.1007/978-3-540-93918-4_34
Publisher Name: Springer, Berlin, Heidelberg
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