Abstract
So far in this book, I have concentrated on the development, implementation and evaluation of various recursive estimation algorithms. But such estimation algo- rithms, no matter how elegant and potentially powerful, are not an end in themselves: they are only worth developing if they perform a practically useful function in the wider context of mathematicalmodelling. In this final chapter, therefore, these estimation algorithms are considered in a more philosophical manner and linked together in terms of their ability to service the requirements of a general approach to stochastic, dynamic modelling from time series data that I have called Data-Based Mechanistic (DBM) modelling.
Keywords
- Proper Orthogonal Decomposition
- Model Order Reduction
- High Order Model
- Transfer Function Model
- Advection Dispersion Equation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2011 Springer-Verlag Berlin Heidelberg
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Young, P.C. (2011). Data-Based Mechanistic (DBM) Modelling. In: Recursive Estimation and Time-Series Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21981-8_12
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DOI: https://doi.org/10.1007/978-3-642-21981-8_12
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