A new parametric generalized exponential entropy measure on intuitionistic vague sets

2021 ◽  
Author(s):  
Taruna ◽  
H. D. Arora ◽  
Pratiksha Tiwari
Keyword(s):  
Entropy Measure ◽  
Vague Sets ◽  
Exponential Entropy

Some Information Measures for Fuzzy Rough Sets

2021 ◽  
Vol 10 (2) ◽  
pp. 82-102
Author(s):  
Omdutt Sharma ◽  
Pratiksha Tiwari ◽  
Priti Gupta
Keyword(s):  
Information Theory ◽  
Rough Sets ◽  
Real Life ◽  
Conditional Entropy ◽  
Scientific Data ◽  
Entropy Measure ◽  
Vague Sets ◽  
Medical Diagnoses ◽  
Information Measures ◽  
Decision Making Problem

Information theory is a tool to measure uncertainty; these days, it is used to solve various challenging problems that involve hybridization of information theory with the fuzzy set, rough sets, vague sets, etc. In order to solve challenging problems in scientific data analysis and visualization recently, various authors are working on hybrid measures of information theory. In this paper, using the relation between information measures, some measures are proposed for the fuzzy rough set. Firstly, an entropy measure is derived using the fuzzy rough similarity measure, and then corresponding to this entropy measure, some other measures like mutual information measure, joint entropy measure, and conditional entropy measure are also proposed. Some properties of these measures are also studied. Later, the proposed measure is compared with some existing measures to prove its efficiency. Further, the proposed measures are applied to pattern recognition, medical diagnoses, and a real-life decision-making problem for incorporating software in the curriculum at the Department of Statistics.

On Weighted Exponential Entropies

2001 ◽  
Vol 92 (1) ◽  
pp. 3-7
Author(s):  
Tarald O. Kvålseth
Keyword(s):  
Entropy Function ◽  
Entropy Measure ◽  
Exponential Entropy ◽  
Classical Entropy

As an alternative to Shannon's classical entropy measure of information, an exponential entropy function was proposed by Pal and Pal in 1989 and 1991. To generalize Pal's entropy further, this author introduced two different families of exponential entropies that are one-parameter generalizations of Pal's entropy. The purpose of the present paper is to define weighted entropies corresponding to those one-parameter generalizations. Some properties and examples of such weighted exponential entropies are discussed.

Generalized Exponential Entropy on Intuitionistic Fuzzy Sets

2014 ◽  
Vol 556-562 ◽  
pp. 4097-4102 ◽  
Author(s):  
Lan Zhen Yang ◽  
Xiao Ying Gong ◽  
Xian Jie Wang ◽  
Sheng Qiao An
Keyword(s):  
Fuzzy Sets ◽  
Intuitionistic Fuzzy Sets ◽  
Fuzzy Entropy ◽  
Entropy Measure ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Entropy ◽  
Exponential Entropy

Based on the concept of intuitionistic fuzzy entropy, this paper proposes a generalized parametric exponential intuitionistic fuzzy entropy measure. This measure is a generalized version of exponential intuitionistic fuzzy entropy proposed by Verma and Sharma, and its validity as an intuitionistic fuzzy entropy is verified. Further, some interesting properties of this measure are also analyzed, and an example shows that this measure is nonmonotone.

A novel q-rung orthopair fuzzy TODIM approach for multi-criteria group decision making based on Shapley value and relative entropy

2021 ◽  
Vol 40 (1) ◽  
pp. 235-250
Author(s):  
Liuxin Chen ◽  
Nanfang Luo ◽  
Xiaoling Gou
Keyword(s):  
Decision Making ◽  
Relative Entropy ◽  
Group Decision Making ◽  
Shapley Value ◽  
Group Decision ◽  
Similarity Measures ◽  
Entropy Measure ◽  
Shapley Values ◽  
Interactive Relationship ◽  
The Difference

In the real multi-criteria group decision making (MCGDM) problems, there will be an interactive relationship among different decision makers (DMs). To identify the overall influence, we define the Shapley value as the DM’s weight. Entropy is a measure which makes it better than similarity measures to recognize a group decision making problem. Since we propose a relative entropy to measure the difference between two systems, which improves the accuracy of the distance measure.In this paper, a MCGDM approach named as TODIM is presented under q-rung orthopair fuzzy information.The proposed TODIM approach is developed for correlative MCGDM problems, in which the weights of the DMs are calculated in terms of Shapley values and the dominance matrices are evaluated based on relative entropy measure with q-rung orthopair fuzzy information.Furthermore, the efficacy of the proposed Gq-ROFWA operator and the novel TODIM is demonstrated through a selection problem of modern enterprises risk investment. A comparative analysis with existing methods is presented to validate the efficiency of the approach.

Feature extraction method of pipeline signal based on parameter optimized vocational mode decomposition and exponential entropy

2021 ◽  
pp. 014233122110294
Author(s):  
Yina Zhou ◽  
Yong Zhang ◽  
Jingyi Lu ◽  
Fan Yang ◽  
Hongli Dong ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Working Conditions ◽  
Acoustic Signals ◽  
Normal Operation ◽  
Local Characteristic ◽  
Support Vector ◽  
K Value ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Exponential Entropy

Pipeline leakage is the main reason that affects normal operation of the pipeline. In this paper, a feature recognition method for pipeline acoustic signals based on vocational mode decomposition (VMD) and exponential entropy (EE) is investigated, which could extract the characteristics of pipeline signals and further accurately identify the pipeline acoustic signals under different working conditions. First, the VMD is used to decompose the collected acoustic signals into a number of mode components, during which process the optimal mode number (i.e., K-value) is determined by combining local characteristic scale decomposition (LCD) and correlation analysis methods. Then, the characteristic content of each mode component is analyzed with the help of the determined correlation coefficient (CC) threshold. If the correlation coefficient of a mode component is greater than the threshold, then the mode component is selected as the feature component. Subsequently, the EE values of the selected feature components are calculated to form the feature vectors corresponding to different kinds of pipeline signals. Finally, the feature vectors are input into support vector machine (SVM) to classify and recognize the different pipeline states. The experimental results demonstrate that the proposed method can identify the pipeline signals under different working conditions, and the recognition accuracy is up to [Formula: see text]. By analyzing and comparing with methods of EE-SVM, original data-SVM, VMD-singular spectrum entropy (SSE) and VMD-information entropy (IE), it is further verified that the proposed method is feasible and superior to the methods.

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