Data-based mechanistic modelling, forecasting, and control.

Chotai, Arun and Young, Peter C. (2001) Data-based mechanistic modelling, forecasting, and control. IEEE Control Systems Magazine, 21 (5). pp. 14-27. ISSN 0272-1708

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Abstract

This article briefly reviews the main aspects of the generic data based mechanistic (DBM) approach to modeling stochastic dynamic systems and shown how it is being applied to the analysis, forecasting, and control of environmental and agricultural systems. The advantages of this inductive approach to modeling lie in its wide range of applicability. It can be used to model linear, nonstationary, and nonlinear stochastic systems, and its exploitation of recursive estimation means that the modeling results are useful for both online and offline applications. To demonstrate the practical utility of the various methodological tools that underpin the DBM approach, the article also outlines several typical, practical examples in the area of environmental and agricultural systems analysis, where DBM models have formed the basis for simulation model reduction, control system design, and forecasting

Item Type:
Journal Article
Journal or Publication Title:
IEEE Control Systems Magazine
Additional Information:
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Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/ge
Subjects:
?? GE ENVIRONMENTAL SCIENCES ??
ID Code:
21590
Deposited By:
Deposited On:
22 Jan 2009 11:52
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Sep 2023 03:49