A Matlab toolbox for grey clustering and fuzzy comprehensive evaluation

Wen, Kun-Li

doi:10.1016/j.advengsoft.2006.12.002

Cited by 31 publications

(15 citation statements)

References 1 publication

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“…Evaluation methods are divided into subjective weight empowerment evaluation methods and objective weight empowerment evaluation methods, both have their own characteristics, the main difference is how to determine the weight [4] . The former is a kind of qualitative analysis, which mostly empowers the indicators through subjective experience; the latter is a kind of quantitative evaluation method, which mainly uses the relationship between the indicators or variation coefficient of indicators to determine the weight.…”

Section: B Ahp-fce Methods Selectementioning

confidence: 99%

Comprehensive evaluation study for urban emergency rescue capability

2011

2011 2nd IEEE International Conference on Emergency Management and Management Sciences

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This article first takes full accounts of the properties of urban emergency rescue capability, and the features of each evaluation method, then describes the procedures and principles for evaluate urban emergency rescue capabilities with the combination of AHP(Analytic Hierarch Process) and FCE(Fuzzy Comprehensive Evaluation) method. At last, with case study, establishing the urban emergency rescue capability evaluation system, using of AHP to determine weights, and take southern city Z as an example for evaluation with FCE method, and provided an effective reference model for evaluation of urban emergency rescue capability.

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Section: B Ahp-fce Methods Selectementioning

confidence: 99%

Comprehensive evaluation study for urban emergency rescue capability

2011

2011 2nd IEEE International Conference on Emergency Management and Management Sciences

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“…Tseng used DEA Malmquist and other productivity index analysis methods to evaluate China's green innovation ability [43]. However, the aforementioned evaluation methods often inevitably lead to subjective assumptions being made by experts and unreasonable weight allocations, ignoring the randomness of evaluation information and the relationships between indicators [44][45][46]. Fuzziness and randomness are interrelated, and although the aforementioned methods have advantages in analyzing complex evaluation factors, it is regrettable that the inherent randomness of the information is ignored.…”

Section: Introductionmentioning

confidence: 99%

Research on the Evaluation of Green Innovation Capability of Manufacturing Enterprises in Innovation Network

Zhai

2020

Sustainability

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The aims of this paper are to objectively evaluate the green innovation ability of manufacturing enterprises in an innovation network and to consider the mutual influence relationship and information randomness of each evaluation index. To do so, 14 indicators were chosen to capture the innovation environment, input, and output, and to construct an evaluation index system. The cloud model method is used to express the evaluation information to assign a quantitative value to qualitative information, and this method is combined with the entropy method to determine the initial weight of the indices. Then, the cloud model and decision-making trial and evaluation laboratory (DEMATEL) method are combined to determine the final comprehensive weight of the indices, and the similarity between each value and ideal value is determined by the cloud distance measurement method and the grey correlation analysis method. Finally, the effectiveness and stability of this method and an accurate numerical method are verified in an empirical analysis that evaluates the green innovation ability of different enterprises. The results demonstrate the superiority of the evaluation system proposed in this paper, which can provide a theoretical basis for the evaluation of the green innovation ability of enterprises.

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“…Fuzzy comprehensive evaluation model [4] is a general method to solve such kind of problem, which has two important ingredients: one is fuzzy relation matrix, the other is the weight vector of evaluation factors. Usually expert knowledge is used to establish them, which has two defects: one is that the expert knowledge and experience may introduce more subjectivity into evaluation process; the other is that the method would bring heavy load to the experts, which make it hard to implement automatically.…”

Section: Introductionmentioning

confidence: 99%

A multilevel fuzzy comprehensive evaluation model for test paper quality

Wang

Ting-ting

Juan

2008

2008 IEEE International Conference on Granular Computing

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Evaluation of test paper quality plays an increasingly important role in education management. But there is no effective approach to implementing it automatically. The paper proposes a multilevel fuzzy comprehensive evaluation model for test paper quality based on education statistics. The model has 2 levels. The first level includes seven basic factors: difficulty, difference, credibility, eligible rate, overlay rate, kurtosis and skewness. The second level includes the degree-3, rate-2 and normality. Degree-3 consists of difficulty, difference and credibility; Rate-2 is composed of eligible rate and overlay rate; Normality includes kurtosis and skewness. Experiments show that the method can evaluate test paper quality effectively.

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A Matlab toolbox for grey clustering and fuzzy comprehensive evaluation

Cited by 31 publications

References 1 publication

Comprehensive evaluation study for urban emergency rescue capability

Comprehensive evaluation study for urban emergency rescue capability

Research on the Evaluation of Green Innovation Capability of Manufacturing Enterprises in Innovation Network

A multilevel fuzzy comprehensive evaluation model for test paper quality

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