Continuous-time model identification from sampled data: Implementation issues and performance evaluation

The identification and estimation of rainfall-flow models is one of the most challenging problems in hydrology. This paper presents the results of direct continuous-time identification and estimation of a rainfall-flow model for the Canning, an ephemeral river in Western Australia, based on daily sampled data. It compares simplified and full implementations of the optimal instrumental variable algorithm used in the application and discusses the advantages of this direct method when compared with the alternative indirect approach to the problem based on discrete-time model estimation.

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“…;Johansson, et al, 1999;Bastogne et al, 2001;Wang and Gawthrop, 2001;Garnier et al, 2003;Garnier and Young, 2004;Moussaoui et al, 2005).…”

Section: Optimal IV Methods For Ct Modelsmentioning

confidence: 99%

Identification and Estimation of Continuous-Time Rainfall-Flow Models

Young

Garnier

2006

IFAC Proceedings Volumes

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“…Interest in CT approaches to system identification has however been growing in the last fifteen years. Furthermore, some recent publications have drawn attention to difficulties that can be encountered when utilizing DT estimation algorithms under conditions that are non-standard, such as rapidly sampled data and systems with widely different natural frequencies (Garnier et al, 2003), (Ljung, 2003).…”

Section: This Papermentioning

confidence: 99%

Continuous-Time Model Identification of Robot Flexibilities for Fast Visual Servoing

Cuvillon

Laroche

Garnier

et al. 2006

IFAC Proceedings Volumes

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“…Standard pre-processing methods as the statevariable filtering (SVF) or the Generalised Poisson moment functional (GPMF) techniques combined with least squares (LS) and sub-optimal instrumental variables (IV) have been implemented (Garnier et al [2003b). …”

Section: Linear Time-invariant Transfer Function Modelsmentioning

confidence: 99%

“…• it supports most of the time-domain methods developed over the last thirty years (Garnier et al [2003b]) for identifying linear dynamic continuous-time parametric models from measured input/output sampled data; • it provides transfer function and state-space model identification methods for single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems, including both traditional and more recent approaches; • it can handle mild irregularly sampled data in a straightforward way; • it may be seen as an add-on to the system identification (SID) toolbox for MATLAB . To facilitate its use, it has been given a similar setup to the SID toolbox; • it provides a flexible graphical user interface (GUI) that lets the user analyse the experimental data, identify and evaluate models in an easy way.…”

Section: Introductionmentioning

confidence: 99%