A Method of Parameter Identification for Dynamic Systems Based on Model Output Minimum Entropy

Author

Taiyuan, Liu ; Jianfang, Jia ; Hong, Wang ; Hong, Yue

Author_Institution

Chinese Acad. of Sci., Beijing

fYear

2007

Firstpage

112

Lastpage

114

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.

Keywords

diseases; maximum likelihood estimation; minimum entropy methods; probability; HIV/AIDS model; dynamic systems; maximum likelihood method; model output minimum entropy; parameter estimation; parameter identification method; probability density function; Acquired immune deficiency syndrome; Automation; Entropy; Histograms; Human immunodeficiency virus; Mathematical model; Maximum likelihood estimation; Parameter estimation; Probability density function; Dynamic systems; Entropy; Histogram; Parameter estimation; Probability Density Function;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference, 2007. CCC 2007. Chinese

Conference_Location

Hunan

Print_ISBN

978-7-81124-055-9

Electronic_ISBN

978-7-900719-22-5

Type

conf

DOI

10.1109/CHICC.2006.4346781

Filename

4346781