Linear regressive model structures for estimation and prediction of compartmental diffusive systems
Vries, D. and Keesman, K.J. and Zwart, H.J. (2006) Linear regressive model structures for estimation and prediction of compartmental diffusive systems. In: MATHMOD 2006, Vienna International Conference on Mathematical Modelling, February 7-10, 2006, Vienna, Austria.
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|Abstract:||In input-output relations of (compartmental) diffusive systems, physical parameters appear non-linearly, resulting in the use of (constrained) non-linear parameter estimation techniques with its short-comings regarding
global optimality and computational effort. Given a LTI system in state space form, we propose an approach to get a linear regressive model structure and output predictor, both in algebraic form. We deduce the linear regressive model from a particular LTI state space system without the need of direct matrix inversion. As an example, two cases are discussed, each one a diffusion process which is approximated by a state space discrete time model with n compartments in the spatial plane. After a sequence of steps the system output can then be explicitly predicted by ˆyk = ˆθT φk−n−ˇγk−n as a function of n, sensor and actuator position, the parameter vector θ, and input-output data. This method is attractive for estimation insight in experimental design issues, when physical knowledge in the model structure is to be preserved.
|Item Type:||Conference or Workshop Item|
Electrical Engineering, Mathematics and Computer Science (EEMCS)
|Link to this item:||http://purl.utwente.nl/publications/62371|
|Export this item as:||BibTeX|
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