Pharmacokinetic parameter estimations by minimum relative entropy method

1995 ◽  
Vol 23 (5) ◽  
pp. 479-494 ◽  
Author(s):  
Takashi Amisaki ◽  
Shinto Eguchi
Keyword(s):  
Pharmacokinetic Parameter ◽  
Relative Entropy ◽  
Entropy Method ◽  
Parameter Estimations ◽  
Minimum Relative Entropy ◽  
Relative Entropy Method

Effects of a priori parameter selection in minimum relative entropy method on inverse electrocardiography problem

2017 ◽  
Vol 26 (6) ◽  
pp. 877-897 ◽  
Author(s):  
Onder Nazim Onak ◽  
Yesim Serinagaoglu Dogrusoz ◽  
Gerhard Wilhelm Weber
Keyword(s):  
Relative Entropy ◽  
A Priori ◽  
Parameter Selection ◽  
Entropy Method ◽  
Inverse Electrocardiography ◽  
Minimum Relative Entropy ◽  
Relative Entropy Method

Minimum Relative Entropy Criterion for Damage Detection and Location

2007 ◽  
Vol 347 ◽  
pp. 421-426 ◽  
Author(s):  
Andreas Kyprianou ◽  
Cristinel Mares ◽  
Charalambos D. Charalambous ◽  
John E. Mottershead
Keyword(s):  
Damage Detection ◽  
Frequency Response ◽  
Relative Entropy ◽  
Low Frequency ◽  
Entropy Method ◽  
Linear Inverse Problems ◽  
Minimum Relative Entropy ◽  
Relative Entropy Method ◽  
Detection And Localization ◽  
Entropy Criterion

Relative entropy has been employed, as an alternative to other regularization methods, in solving ill-conditioned linear inverse problems. Damage detection when treated as structural modification imparted by the damage leads to a linear inverse problem involving frequency response functions. This problem is amenable to ill-conditioning issues that could arise from the low frequency response values and noisy experiments. This article formulates and solves using the minimum relative entropy method the damage detection and localization problem on a simulated cantilever beam.

scholarly journals Relative Entropy Method Applied for the Fatigue Life Distribution of Carbon Fiber/Epoxy Composites

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 224
Author(s):  
Changsheng Yuan ◽  
Yingjie Liang
Keyword(s):  
Experimental Data ◽  
Carbon Fiber ◽  
Relative Entropy ◽  
Least Squares Method ◽  
Entropy Method ◽  
Epoxy Composites ◽  
Order Moment ◽  
Kolmogorov Smirnov ◽  
Relative Entropy Method ◽  
Carbon Fiber Epoxy

This paper verifies the feasibility of the relative entropy method in selecting the most suitable statistical distribution for the experimental data, which do not follow an exponential distribution. The efficiency of the relative entropy method is tested through the fractional order moment and the logarithmic moment in terms of the experimental data of carbon fiber/epoxy composites with different stress amplitudes. For better usage of the relative entropy method, the efficient range of its application is also studied. The application results show that the relative entropy method is not very fit for choosing the proper distribution for non-exponential random data when the heavy tail trait of the experimental data is emphasized. It is not consistent with the Kolmogorov–Smirnov test but is consistent with the residual sum of squares in the least squares method whenever it is calculated by the fractional moment or the logarithmic moment. Under different stress amplitudes, the relative entropy method has different performances.

A COMBINATION WEIGHTING ALGORITHM USING RELATIVE ENTROPY FOR DOCUMENT CLUSTERING

2014 ◽  
Vol 28 (03) ◽  
pp. 1453002 ◽  
Author(s):  
BO JI ◽  
YANGDONG YE ◽  
YU XIAO
Keyword(s):  
Relative Entropy ◽  
Document Clustering ◽  
Multiple Attribute Decision Making ◽  
Entropy Method ◽  
Data Object ◽  
Information Bottleneck ◽  
Subjective Weight ◽  
Multiple Attribute ◽  
Sequential Information ◽  
Relative Entropy Method

This paper proposes a combination weighting algorithm using relative entropy for document clustering. Combination weighting is widely used in multiple attribute decision making (MADM) problem. However, there exist two difficulties to hinder the applications of combination weighting on document clustering. First, combination weighting is based on the integration of subjective weighting and objective weighting. However, there are so many attributes in documents that the subjective weights which rely on manual annotation by experts are impracticable. Secondly, a document data object might contain hundreds or even thousands of features. It is an extremely time-consuming task to calculate the combination weights. To address the issues, we suggest to simplify the combination weighting by not distinguishing subjective weight and objective weight. Meanwhile, we choose relative entropy method to reduce running time. In our algorithm, we obtain a combination weight set with 14 combination forms. The experiments on real document data show that both on the AC/PR/RE measures and the mutual information (MI) measure, the proposed CWRE-sIB algorithm is superior to the original sequential information bottleneck (sIB) algorithm and a series of weighting-sIB algorithms, which are built by applying a single weighting scheme to the original sIB algorithm.

A relative entropy method to measure non-exponential random data

2015 ◽  
Vol 379 (3) ◽  
pp. 95-99 ◽  
Author(s):  
Yingjie Liang ◽  
Wen Chen
Keyword(s):  
Relative Entropy ◽  
Entropy Method ◽  
Random Data ◽  
Relative Entropy Method

Improving agent-based negotiation efficiency in construction supply chains: A relative entropy method

2009 ◽  
Vol 18 (7) ◽  
pp. 975-982 ◽  
Author(s):  
Xiaolong Xue ◽  
Qiping Shen ◽  
Heng Li ◽  
William J. O'Brien ◽  
Zhaomin Ren
Keyword(s):  
Supply Chains ◽  
Relative Entropy ◽  
Entropy Method ◽  
Agent Based ◽  
Relative Entropy Method
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