Construction mechanism of whitenization weight function and its application in grey clustering evaluation

Xie, Naiming; Su, Bentao; Chen, Nanlei

doi:10.21629/jsee.2019.01.12

Cited by 23 publications

(7 citation statements)

References 3 publications

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“…(2018) propose a modified grey variable weight clustering method to evaluate enterprise credit risk. Xie et al. (2019) develop a new type of the whitenization weight function by studying the mechanism and nature of the construction of different forms of the function.…”

Section: Methodsmentioning

confidence: 99%

A novel quality risk evaluation framework for complex equipment development integrating PHFS-QFD and grey clustering

Wang,

Wang

et al. 2023

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PurposeThe purpose of this research is to provide a theoretical framework for complex equipment quality risk evaluation. The primary aim of the framework is to enhance the ability to identify risks and improve risk control efficiency during the development phase.Design/methodology/approachA novel framework for quality risk evaluation in complex equipment is proposed, which integrates probabilistic hesitant fuzzy set-quality function deployment (PHFS-QFD) and grey clustering. PHFS-QFD is applied to identify the quality risk factors, and grey clustering is used to evaluate quality risks in cases of poor quality information during the development stage. The unfolding function of QFD is applied to simplify complex evaluation problems.FindingsThe methodology presents an innovative approach to quality risk evaluation for complex equipment development. The case analysis demonstrates that this method can efficiently evaluate the quality risks for aircraft development and systematically trace back the risk factors through hierarchical relationships. In comparison to traditional failure mode and effects analysis methods for quality risk assessment, this approach exhibits superior effectiveness and reliability in managing quality risks for complex equipment development.Originality/valueThis study contributes to the field by introducing a novel theoretical framework that combines PHFS-QFD and grey clustering. The integration of these approaches significantly improves the quality risk evaluation process for complex equipment development, overcoming challenges related to data scarcity and simplifying the assessment of intricate systems.

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Section: Methodsmentioning

confidence: 99%

A novel quality risk evaluation framework for complex equipment development integrating PHFS-QFD and grey clustering

Wang,

Wang

et al. 2023

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“…Grey systems theory has been used on a large scale in both theoretical and practical applications and it works in an environment with a high level of uncertainty (Liu and Lin 2011;). Among the elements of novelty brought about by the grey systems theory and the methods offered as a tool for solving various problems, grey clustering represents one of the major instruments that can be used for the proper classification of the analyzed objects into clusters based on various evaluation rules (Xie et al 2019). As grey systems theory offers good results when only a small amount of data is available, it has been the first choice of many researchers interested in solving problems in various research fields (Delcea 2015).…”

Section: Literature Review On Grey Clusteringmentioning

confidence: 99%

“…The reader can further consider Dong et al (2018) for a comprehensive overview of the applications of grey clustering and the works of Xie et al (2019), Liu and Lin (2011), Xie (2018), andLiu et al (2015) for practical examples accompanied by step-bystep explanations on solving such problems when dealing with grey clustering. Liu et al (2020) opened up new research directions in grey systems theory, whereas Tao et al (2022) provide a bibliometric analysis of grey systems theory in engineering.…”

Section: Literature Review On Grey Clusteringmentioning

confidence: 99%

Portfolio management under capital market frictions: a grey clustering approach

Ţilică,

Dragotă,

Delcea

et al. 2024

Financ Innov

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International portfolio management is influenced by the existence of “frictions”, factors or events that interfere with trade, which are linked in financial literature to market-specific factors, such as available information, restrictions, investor protection, or market liquidity. Given the wide variety of factors that can be included in these categories, scientific studies typically focus on a reduced number of indicators at a time in order to offer an in depth analysis of their impact. We offer a consolidated view of the perspectives observed in financial literature by proposing a novel index for market frictions that includes all these four components and rank fifteen post-communist East European capital markets based on their index values. We then constructed various scenarios by assuming different levels of importance for the criteria used in index construction. By employing grey clustering analysis, we cluster these capital markets into three categories—strongly recommended, recommended with some reserve, and not recommended—based on the importance given by the decision maker to these factors. The results show that some of the studied markets are in the same cluster, irrespective of the chosen scenario. The only market always included in the “strongly recommended” category is Hungary, indicating that it is a good investment option for international participants. Bulgaria and Slovakia are always regarded as “recommended with reserve” markets, whereas the Republic of Moldova is part of the “not recommended” category. The other markets show a degree of variability that can be explained by different investor perspectives. This study contributes to the existing literature by combining the advantages of grey clustering and portfolio analysis. Investors can use this approach during the decision-making process related to their investments.

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“…The whitening weight function is generally a piecewise linear function depending on the turning point. Common whitening weight functions include typical whitening weight function, upper limit measure whitening weight function, lower limit measure whitening weight function and moderate measure whitening weight function [29], as shown in Fig. 3.…”

Section: ) Introduction To the Basic Form Of Whitening Weight Functionmentioning

confidence: 99%

A Method of Power Supply Health State Estimation Based on Grey Clustering and Fuzzy Comprehensive Evaluation

Zhao

2023

IEEE Access

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Health state estimation can evaluate the current degradation state of equipment, and the evaluation results can provide a basis for formulating equipment maintenance strategies. At present, in the research on health state estimation, only one level of indicators of the equipment is usually considered, meaning incomplete estimation results. In addition, the current evaluation methods rarely consider the impact of data noise on the evaluation results, which can easily lead to abnormal evaluation results. To solve the above two problems, this paper first introduces the state indicators such as ripple voltage and output voltage, as well as the ratio of theoretical use time into the evaluation indicators. Furthermore, a health state estimation method combining grey clustering and fuzzy comprehensive evaluation methods is established. This method can consider the characteristics of multiple data groups at the same time, thus the impact of data noise on the evaluation results can be reduced. During the evaluation, the grey clustering method is used to evaluate the clustering coefficient vector of the power supply under a single group of data. After that, the clustering coefficient vector of multiple groups of data is used as the membership vector of fuzzy comprehensive evaluation, and the fuzzy comprehensive evaluation method is used to evaluate the health state of a power supply under multiple groups of data. An example shows that this method is effective in estimating the health state of a normally degraded power supply.INDEX TERMS Health state estimation, grey clustering method, fuzzy comprehensive evaluation methods, power supply.

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Construction mechanism of whitenization weight function and its application in grey clustering evaluation

Cited by 23 publications

References 3 publications

A novel quality risk evaluation framework for complex equipment development integrating PHFS-QFD and grey clustering

A novel quality risk evaluation framework for complex equipment development integrating PHFS-QFD and grey clustering

Portfolio management under capital market frictions: a grey clustering approach

A Method of Power Supply Health State Estimation Based on Grey Clustering and Fuzzy Comprehensive Evaluation

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