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

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.

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Minimum-relative-entropy method—solution to missing short-baseline problem

Journal of Astrophysics and Astronomy ◽

10.1007/bf02714215 ◽

1986 ◽

Vol 7 (4) ◽

pp. 275-292

Author(s):

R. K. Shevgaonkar

Keyword(s):

Relative Entropy ◽

Entropy Method ◽

Short Baseline ◽

Minimum Relative Entropy ◽

Relative Entropy Method

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Applying the minimum relative entropy method for bimodal distribution in a remanufacturing system

International Journal of Production Economics ◽

10.1016/j.ijpe.2007.11.010 ◽

2008 ◽

Vol 113 (2) ◽

pp. 969-979 ◽

Cited By ~ 9

Author(s):

Xing Bao ◽

Ou Tang ◽

Jianhua Ji

Keyword(s):

Relative Entropy ◽

Bimodal Distribution ◽

Entropy Method ◽

Minimum Relative Entropy ◽

Relative Entropy Method

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

Entropy ◽

10.3390/e23020224 ◽

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.

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A COMBINATION WEIGHTING ALGORITHM USING RELATIVE ENTROPY FOR DOCUMENT CLUSTERING

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001414530024 ◽

2014 ◽

Vol 28 (03) ◽

pp. 1453002 ◽

Cited By ~ 2

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.

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