A method of parameter identification for dynamic systems based on model output minimum entropy

T.Y. Liu; J.F. Jia; H. Wang; H. Yue

doi:10.1109/CHICC.2006.4346781

A method of parameter identification for dynamic systems based on model output minimum entropy

T.Y. Liu, J.F. Jia, H. Wang, H. Yue

Electronic And Electrical Engineering

Research output: Contribution to conference › Proceeding › peer-review

Abstract

Parameter estimation is important in mathematical modeling. The Maximum Likelihood method can be used when the probability density function of observation is known. However, this assumption may not be satisfied in practice. To deal with this problem, a new parameter estimation method for dynamic systems is proposed using the entropy of probability density function for system output viable and two performance functions are also given. To illustrate the effectiveness of this method, HIV/AIDS model is taken as an example to evaluate simulation and results are encouraging.

Original language	English
Pages	112-114
Number of pages	2
DOIs	https://doi.org/10.1109/CHICC.2006.4346781
Publication status	Published - 2007
Event	26th Chinese Control Conference - Zhangjiajie, China Duration: 26 Jul 2007 → 31 Jul 2007

Conference

Conference	26th Chinese Control Conference
Country/Territory	China
City	Zhangjiajie
Period	26/07/07 → 31/07/07

Keywords

dynamic systems
histogram
probability density function
entropy
parameter estimation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/CHICC.2006.4346781

Cite this

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abstract = "Parameter estimation is important in mathematical modeling. The Maximum Likelihood method can be used when the probability density function of observation is known. However, this assumption may not be satisfied in practice. To deal with this problem, a new parameter estimation method for dynamic systems is proposed using the entropy of probability density function for system output viable and two performance functions are also given. To illustrate the effectiveness of this method, HIV/AIDS model is taken as an example to evaluate simulation and results are encouraging. ",

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year = "2007",

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language = "English",

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note = "26th Chinese Control Conference ; Conference date: 26-07-2007 Through 31-07-2007",

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AU - Liu, T.Y.

AU - Jia, J.F.

AU - Wang, H.

AU - Yue, H.

PY - 2007

Y1 - 2007

N2 - Parameter estimation is important in mathematical modeling. The Maximum Likelihood method can be used when the probability density function of observation is known. However, this assumption may not be satisfied in practice. To deal with this problem, a new parameter estimation method for dynamic systems is proposed using the entropy of probability density function for system output viable and two performance functions are also given. To illustrate the effectiveness of this method, HIV/AIDS model is taken as an example to evaluate simulation and results are encouraging.

AB - Parameter estimation is important in mathematical modeling. The Maximum Likelihood method can be used when the probability density function of observation is known. However, this assumption may not be satisfied in practice. To deal with this problem, a new parameter estimation method for dynamic systems is proposed using the entropy of probability density function for system output viable and two performance functions are also given. To illustrate the effectiveness of this method, HIV/AIDS model is taken as an example to evaluate simulation and results are encouraging.

KW - dynamic systems

KW - histogram

KW - probability density function

KW - entropy

KW - parameter estimation

UR - http://ccc.amss.ac.cn/main/index.html

U2 - 10.1109/CHICC.2006.4346781

DO - 10.1109/CHICC.2006.4346781

M3 - Proceeding

SP - 112

EP - 114

T2 - 26th Chinese Control Conference

Y2 - 26 July 2007 through 31 July 2007

ER -

A method of parameter identification for dynamic systems based on model output minimum entropy

Abstract

Conference

Keywords

UN SDGs

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