Determining objective weights with intuitionistic fuzzy entropy measures: A comparative analysis

Chen, Ting‐Yu; Li, Chia-Hang

doi:10.1016/j.ins.2010.07.009

Cited by 261 publications

(114 citation statements)

References 32 publications

(34 reference statements)

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“…In addition, an information entropy technique has been applied to calculate the coefficient of weight and to exploit the useful information of data to a maximum extent. Chen and Li [17] proposed an objective weighting method which employs intuitionistic fuzzy entropy measures to tackle MCDM problems under the intuitionistic fuzzy sets environment. Zhao et.…”

Section: A Information Entropy Theory In Decision-makingmentioning

confidence: 99%

Design Concept Evaluation Based on Rough Number and Information Entropy Theory

Zhu

et al. 2015

2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computi

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Abstract-Concept evaluation at the early phase of product development plays a crucial role in new product development. It determines the direction of the subsequent design activities. However, the evaluation information at this stage mainly comes from experts' judgments, which is subjective and imprecise. How to manage the subjectivity to reduce the evaluation bias is a big challenge in design concept evaluation. This paper proposes a comprehensive evaluation method which combines information entropy theory and rough number. Rough number is first presented to aggregate individual judgments and priorities and to manipulate the vagueness under a group decision-making environment. A rough number based information entropy method is proposed to determine the relative weights of evaluation criteria. The composite performance values based on rough number are then calculated to rank the candidate design concepts. The results from a practical case study on the concept evaluation of an industrial robot design show that the integrated evaluation model can effectively strengthen the objectivity across the decision-making processes.

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Section: A Information Entropy Theory In Decision-makingmentioning

confidence: 99%

Design Concept Evaluation Based on Rough Number and Information Entropy Theory

Zhu

et al. 2015

2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computi

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“…Entropy used to be a thermodynamic concept: it was introduced into information theory in 1948 by C. E. Shannon who put forward the concept of information entropy to measure the level of system chaos or disorder [14][15][16][17][18][19]. Shannon information entropy, which is an objective and applicable method for the determination of weight value, was introduced into the comprehensive assessment.…”

Section: Entropy Weight Modelmentioning

confidence: 99%

Comprehensive Assessment on Sustainable Development of Highway Transportation Capacity Based on Entropy Weight and TOPSIS

Zhao

Suo

2014

Sustainability

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Abstract:With the rapid development of the national economy of China, an increasing need for transportation facilities is becoming a serious challenge that the existing traffic system has to meet. Thus, the highway transportation capacity development level assessment has important significance in theory and in practice. In order to overcome the current defects of stronger subjectivity and experience in common assessment methods, the entropy weight and the TOPSIS method were introduced and employed to the comprehensive assessment of highway transportation capacity development. Shannon information entropy was applied to determine the weight value of each index in the comprehensive assessment model. After determining the index weight, the result of comprehensive assessment was obtained through the TOPSIS method. Finally, the effectiveness and feasibility of the proposed method were shown by application in practice.

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“…Therefore, the entropy of the information can be used to evaluate the orderly degree and effectiveness of the information for a system. As a balanced system, the microgrid operation can be defined as the entropy of internal energy distribution, which is given as follows [28]: (2) (3) where is the rate of energy power distribution of i th unit; is the energy entropy; is the energy power of i th unit; is the total energy power; is the number of units. The vulnerability of a microgrid is described as the ability to maintain the stability of network structure when one element or some elements are out of operation.…”

Section: Operation Vulnerability Indexmentioning

confidence: 99%

The Application of Weighted Entropy Theory in Vulnerability Assessment and On-Line Reconfiguration Implementation of Microgrids

Xue

Xiang

Chen

2014

Entropy

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Abstract:With the consideration of randomness of distributed generations and loads, this paper has proposed a method for vulnerability assessment of microgrids based on complex network theory and entropy theory, which can explain the influence of the inherent structure characteristics and system internal energy distribution on the microgrid. The vulnerability assessment index is built, and the on-line reconfiguration model considering the vulnerability assessment of microgrid is also established. An improved cellular bat algorithm is tested on the CERTS system to implement the real time reconfiguration fast and accurately to provide the basis of theory and practice.

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Determining objective weights with intuitionistic fuzzy entropy measures: A comparative analysis

Cited by 261 publications

References 32 publications

Design Concept Evaluation Based on Rough Number and Information Entropy Theory

Design Concept Evaluation Based on Rough Number and Information Entropy Theory

Comprehensive Assessment on Sustainable Development of Highway Transportation Capacity Based on Entropy Weight and TOPSIS

The Application of Weighted Entropy Theory in Vulnerability Assessment and On-Line Reconfiguration Implementation of Microgrids

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