14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper WeB2.5

Vries, Dirk (Wageningen Univ.), Keesman, Karel (Wageningen Univ.), Zwart, Hans (Univ. of Twente)

Explicit Linear Regressive Model Structures for Estimation, Prediction and Experimental Design in Compartmental Diffusive Systems

Scheduled for presentation during the Regular Session "Identification of Linear Systems I" (WeB2), Wednesday, March 29, 2006, 16:50−17:10, Banquet Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 22, 2018

Keywords Input and Excitation Design, Grey Box Modelling, Continuous Time System Estimation


A linear regressive model structure and output predictor, both in algebraic form, are deduced from an LTI state space system with certain properties without the need of direct matrix inversion. On the basis of this, explicit expressions of parametric sensitivities are given. As an example, a diffusion process is approximated by a state space discrete time model with n compartments in the spatial plane and is then reparametrized. The system output can then be explicitly predicted by yk=θTφk-n-γk-n as a function of n, the sensor position, the parameter vector θ, and input-output data. This method is attractive for estimation, prediction and insight in experimental design issues, when physical knowledge is to be preserved.