Epilogue

Eykhoff, P.

doi:10.1016/b978-0-08-025683-2.50016-8

Cited by 5 publications

(10 citation statements)

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Section: Proposementioning

confidence: 99%

“…In fact, exists 0 τ ≠ , we normally try to use approximate polynomial fraction models with higher degrees of m n , to increase the model accuracy. With this approach, the object identification problem without dead time delay is considered to be completely solved in theory [1,4]. In fact, however, applying the pure polynomial D DAVID PUBLISHING…”

Section: Proposementioning

confidence: 99%

“…With this approach, the object identification problem without dead time delay is considered to be completely solved in theory [1,4]. In fact, however, applying the pure polynomial…”

Section: Proposementioning

confidence: 99%

“…The Theory of Prediction and Identification has been studied and developed with thousands of scientific works made public since last century. We can find the fundamental results of studies of statistics and prediction in [1,2], of kinetics system identification in detail in [3,4].To use linear algebra methods, we often try to change the regressive models into linear combination forms of coefficients: , to increase the model accuracy. With this approach, the object identification problem without dead time delay is considered to be completely solved in theory [1,4].…”

mentioning

confidence: 99%

“…We can find the fundamental results of studies of statistics and prediction in [1,2], of kinetics system identification in detail in [3,4].…”

mentioning

confidence: 99%

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An Application of Random Process for Controlled Object Identification with Traffic Delay Problem

Viet¹

2015

JMSS

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Abstract:In the article proposed an effective method estimating transfer function model of controlled plant including dead-time delay, based on stochatstic time series of input-output signals. The model structure is modified with parameters optimized until the model error becomes "white-noise" series that with inough smal auto-correlation function. ProposeThe Real signals which occur in the control process always imlpy influences of many random factors, so the Directive Object Identification Problem is often related to random process. Mathematically, the Controlled Object Identification problem is the problem that predicts the trend of Random Process:-regressive function that reflects the trend of non-random process or is the model of the identification problem; ( ) u trandom error. The Theory of Prediction and Identification has been studied and developed with thousands of scientific works made public since last century. We can find the fundamental results of studies of statistics and prediction in [1,2], of kinetics system identification in detail in [3,4].To use linear algebra methods, we often try to change the regressive models into linear combination forms of coefficients: , to increase the model accuracy. With this approach, the object identification problem without dead time delay is considered to be completely solved in theory [1,4]. In fact, however, applying the pure polynomial

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Section: Proposementioning

confidence: 99%

Section: Proposementioning

confidence: 99%

“…With this approach, the object identification problem without dead time delay is considered to be completely solved in theory [1,4]. In fact, however, applying the pure polynomial…”

Section: Proposementioning

confidence: 99%

mentioning

confidence: 99%

“…We can find the fundamental results of studies of statistics and prediction in [1,2], of kinetics system identification in detail in [3,4].…”

mentioning

confidence: 99%

See 3 more Smart Citations

An Application of Random Process for Controlled Object Identification with Traffic Delay Problem

Viet¹

2015

JMSS

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Optimal experiment design

Emery

Ненарокомов

1998

Meas. Sci. Technol.

142

134

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Optimal experiment design is the definition of the conditions under which an experiment is to be conducted in order to maximize the accuracy with which the results are obtained. This paper summarizes a number of methods by which the parameters of the mathematical model of the system are estimated and describes the application of the Fisher information matrix. Examples are given for thermal property estimation in which the estimation is affected both by measurement noise, which is present during any experiment, but also by uncertainties in the parameters of the model used to describe the system.

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Sensitivity analysis of biochemical systems using bond graphs

Gawthrop

Pan

2023

J. R. Soc. Interface.

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The sensitivity of systems biology models to parameter variation can give insights into which parameters are most important for physiological function, and also direct efforts to estimate parameters. However, in general, kinetic models of biochemical systems do not remain thermodynamically consistent after perturbing parameters. To address this issue, we analyse the sensitivity of biological reaction networks in the context of a bond graph representation. We find that the parameter sensitivities can themselves be represented as bond graph components, mirroring potential mechanisms for controlling biochemistry. In particular, a sensitivity system is derived which re-expresses parameter variation as additional system inputs. The sensitivity system is then linearized with respect to these new inputs to derive a linear system which can be used to give local sensitivity to parameters in terms of linear system properties such as gain and time constant. This linear system can also be used to find so-called sloppy parameters in biological models. We verify our approach using a model of the Pentose Phosphate Pathway, confirming the reactions and metabolites most essential to maintaining the function of the pathway.

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Epilogue

Cited by 5 publications

References 0 publications

An Application of Random Process for Controlled Object Identification with Traffic Delay Problem

An Application of Random Process for Controlled Object Identification with Traffic Delay Problem

Optimal experiment design

Sensitivity analysis of biochemical systems using bond graphs

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