The determination of the orders of process-and noise dynamics

Boom, A.J.W. van den; Enden, A. W. M. Van Den

doi:10.1016/0005-1098(74)90035-1

Cited by 88 publications

(6 citation statements)

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“…Maximizing information is the same as maximizing choice when selecting a message; and the more freedom, the more uncertainty as to the outcome (Radoski et al 1975 (Cadzow, 1982 Marple and Lawrence, 1987;Bohlin, 1984;Astrom, 1980; van den Boom and van den Enden, 1974 An important argument is made by Brillinger and Tukey to approach the subject by using "leading cases" (Brillinger and Tukey, 1984 (Munk and Snodgrass, 1957 For weakly non-stationary processes, the effect over a small time period is unimportant. If needed, the signal can be cut into relatively stationary sections and spectra found using methods specially designed for short data segments, i.e.…”

Section: Maximum Entropy Methodsmentioning

confidence: 99%

Estimating soil parameters important for lifeline siting using system identification techniques

Glaser¹

1993

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Section: Maximum Entropy Methodsmentioning

confidence: 99%

Estimating soil parameters important for lifeline siting using system identification techniques

Glaser¹

1993

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“…The tests used and the relative weights applied to these tests depends upon the use of the model. Discussions of the subject appear in several places [24,26,28,36,[46][47][48][49][50][51]. Outlined here are the calculations performed in IDENT and their use in model selection.…”

Section: Model Evaluation Criteriamentioning

confidence: 99%

A computer program for linear nonparametric and parametric identification of biological data

Werness

Anderson

1984

Computer Programs in Biomedicine

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A computer program package for parametric ad nonparametric linear system identification of both static and dynamic biological data, written for an LSI-11 minicomputer with 28 K of memory, is described. The program has 11 possible commands including an instructional help command. A user can perform nonparametric spectral analysis and estimation of autocorrelation and partial autocorrelation functions of univariate data and estimate nonparametrically the transfer function and possibly an associated noise series of bivariate data. In addition, the commands provide the user the means to derive a parametric autoregressive moving average model for univariate data, to derive a parametric transfer function and noise model for bivariate data, and to perform several model evaluation tests such as pole-zero cancellation, examination of residual whiteness and uncorrelatedness with the input. The program, consisting of a main program and driver subroutine as well as six overlay segments, may be run interactively or automatically.

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“…Rather more unorthodox approaches to model structure identification include lvakhnenko's (1968) group method of data handling (GMDH) algorithm (see also Chapter 13 by lvakhnenko et al in this volume) and the application of methods of pattern recognition (Kittler and Whitehead, 1976). For other reviews of specific details of this problem the reader is referred to the papers by Van den Boom and Van den Enden (1974) and Unbehauen and Gohring (1974).…”

Section: Model Structure Identification: Black Box Modelsmentioning

confidence: 99%

Model Structure Identification From Experimental Data

Beck¹

1979

Theoretical Systems Ecology

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PREFACEIn recent years there has been considerable interest in the development of models for river and lake ecological systems. Much of this interest has been directed toward the development of progressively larger and more complex simulation models. In contrast, relatively little attention has been devoted to the problems of uncertainty and errors in the field data, of inadequate numbers of field data, of uncertainty in the relationships between the important system variables, and of uncertainty in the model parameter estimates. The International Institute for Applied Systems Analysis Resources and Environment Area's Task on Models for Environmental Quality Control and Management addresses problems such as these.The subject of this paper is model calibration. But rather than solving the customary problem of model parameter estimation, given an established structure for the model, the paper attempts to answer the prior question of identifying the dominant relationships between the system inputs, state variables, and output responses. That, then, is the problem that needs to be solved before one can consider how to estimate the model parameter values accurately. And it is a problem because, despite very many laboratory-scale experiments and a number of major field studies, our knowledge of the relationships between the mineral, organic, and microbiological components of an aquatic ecosystem is still quite uncertain.iii SUMMARY This paper is reprinted from the book Theoretical Systems Ecology: Advances and Case Studies, edited by E. Halfon of the Canada Centre for Inland Waters and published by Academic Press, New York. Acknowledging that systems ecology has had a large impact on all aspects of environmental research, the book aims to bridge the gap in communication between theoreticians, modelers, and field ecologists. Three classes of problems are treated in the book. They separate approximately into (a) how the model should be developed and analyzed prior to the collection and use of field data, (b) how the model should be developed or modified when it is evaluated against field data, and (c) the desired properties of the models for control and management purposes. The objective of this paper is to emphasize the fact that the second of these three problem categories is not simply a matter of straightforward model parameter estimation.The basic problem of model calibration -or system identification -is that information about the "externally" observed behavior of a system is required to be translated into information about the model-based description of the system's "internal" mechanisms of behavior. The measured input and output variables represent the system's external description, whereas state variables and parameter values refer to the system's internal description. In other words, during model calibration, and especially in the process of identifying the model's structure, we seek improved understanding of those physical, chemical, and biological phenomena that are thought to govern the system's observed be...

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The determination of the orders of process-and noise dynamics

Cited by 88 publications

References 1 publication

Estimating soil parameters important for lifeline siting using system identification techniques

Estimating soil parameters important for lifeline siting using system identification techniques

A computer program for linear nonparametric and parametric identification of biological data

Model Structure Identification From Experimental Data

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