Distributed parameter system indentification A survey†

Kubrusly, Carlos S.

doi:10.1080/00207177708922326

Cited by 142 publications

(29 citation statements)

References 95 publications

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“…Because the aim of this chapter is to encourage the use of extended Kalman filtering in groundwater modeling, the equations are presented without rigorous mathematical derivations or proofs. Readers interested in these mathematical details should refer to Tzafestas and Nightingale (1968a, 1968b, 1969, Jazwinski (1970), Gelb (1974), Kubrusly (1977), Ljung (1979), and Ljung and Soderstrom (1983).…”

Section: General Formulation Of Extended Kalman Filtering Equationsmentioning

confidence: 99%

Estimation and Prediction of Hydrogeochemical Parameters Using Extended Kalman Filtering

Graham¹

2002

Stochastic Methods in Subsurface Contaminant Hydrology

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Section: General Formulation Of Extended Kalman Filtering Equationsmentioning

confidence: 99%

Estimation and Prediction of Hydrogeochemical Parameters Using Extended Kalman Filtering

Graham¹

2002

Stochastic Methods in Subsurface Contaminant Hydrology

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“…With enough measured data, however, the statistical parameter estimation approach (i.e., solving (2) for parameters on the basis of the data) can be attempted, at least for exploratory data analysis. Much work has been done in this field by proponents of the system identification school; see reviews by Kubrusly (1977) Chavent (1979 Raibman et al (1982) and a monograph by Bennett (1979).…”

Section: Possible Approaches To Parameter Estimationmentioning

confidence: 99%

Identification of motion parameters from spatio-temporal atmospheric temperature patterns

Fradkin¹,

Knox²

1985

Boundary-Layer Meteorol

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In near-calm conditions it is difficult to make direct measurements of atmospheric advection reliably and cheaply, particularly at many points over a large area. An alternative indirect method is examined using time-series measurements of atmospheric temperature (or any other convenient conserved field variable) at points on a spatial grid.System identification methodology is applied to analyze atmospheric temperature data obtained in such near-calm conditions during an experiment with a low-flying helicopter. A three-parameter numerical model of atmospheric advection has been identified in the temperature data, the advection parameters being an eddy diffusion coefficient and horizontal components of the wind velocity. It has been demonstrated by analysis of all the assumptions, and using simulated data, that in this case the standard least-square procedure can be applied to recover sensible parameter estimates, even though the model is of the error-in-variables type and parameters appear to change abruptly at certain moments of time (but by a reasonably small amount). The parameter estimates have been validated against independent data.

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“…Though a lot of studies have been carried out on the identification of the DPS (see surveys by Kubrusly 1977, Polis and Goodson 1976, Ruberti 1978, and Sagara and Nakano 1980, it is noted that most of them are mainly concerned with the estimation of unknown parameters in the DPS from input and output data. However, the identification of the external input applied to the DPS is becoming increasingly important in addition to the parameter estimation, in relation to the analysis of geophysical and environmental problems.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous identification of the external input and parameters of diffusion type distributed parameter systems

Ohnaka

Uosaki

1987

International Journal of Control

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A simultaneous identification method is proposed for an unknown diffusion constant and an unknown external input of a distributed parameter system governed by a non-homogeneous partial differential equation of parabolic (diffusion) type. The mathematical formulation of the identification problem is presented using the weighted residual expression and the boundary element partition, both of which are fundamental to the boundary element method. The unknowns of the distributed parameter systems are identified from the noisy or noise-free state observations taken at the points on the boundary by minimizing a certain criterion function based on the above mathematical formulation. Numerical examples illustrate the usefulness of the proposed identification method.

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Distributed parameter system indentification A survey†

Cited by 142 publications

References 95 publications

Estimation and Prediction of Hydrogeochemical Parameters Using Extended Kalman Filtering

Estimation and Prediction of Hydrogeochemical Parameters Using Extended Kalman Filtering

Identification of motion parameters from spatio-temporal atmospheric temperature patterns

Simultaneous identification of the external input and parameters of diffusion type distributed parameter systems

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