Extending the Kalman filter for structured identification of linear and nonlinear systems

This paper considers a novel approach to system identification which allows accurate models to be created for both linear and nonlinear multi-input / output systems. In addition to conventional system identification applications the method can also be used as a black-box tool for model order reducti...

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Bibliographic Details

Main Authors:	Matt Best, Karol Bogdanski
Format:	Default Article
Published:	2016
Subjects:	Other engineering not elsewhere classified Artificial intelligence not elsewhere classified System identification Model order reduction Kalman filter Linear systems Nonlinear systems Artificial Intelligence and Image Processing Engineering not elsewhere classified
Online Access:	https://hdl.handle.net/2134/20708
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