Multiple Attribute Variable Weight Fuzzy Decision-Making Based on Optimistic Coefficient Method

Sun, Peng; Chen, Shunping; Zhi, Yongfeng

doi:10.1007/s40815-020-01020-7

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Chen et al [21] proposed a two-stage logic scoring of the preference-ELECTRE III-based approach. Sun et al [22] proposed an iterative weight update method based on the optimistic coefficient.…”

Section: Requirement Analysis Of Intelligent Product Designmentioning

confidence: 99%

Knowledge Service Technology for Supporting Intelligent Product Design

Wang

Niu

2021

Shock and Vibration

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In this paper, a knowledge service method that supports the intelligent design of products is investigated. The proposed method provides the solutions to computational problems and reasoning and decision-making problems in the field of intelligent design. The requirement analysis of a knowledge-based intelligent design system integrates design knowledge into case-based reasoning activities through scheme analysis, scheme evaluation, and scheme adjustment, thus achieving knowledge-based intelligent reasoning and decision-making. During the similarity matching, a new hybrid similarity measurement method is proposed to calculate the similarity of crisp and fuzzy sets. This method integrates the fuzzy set similarity theory based on the traditional similarity measurement method. A method of attribute level classification is proposed to assign weight coefficients. The attributes are divided into the primary matching and auxiliary matching levels according to the decisiveness of case matching, and the set of weight coefficients is continuously and dynamically updated through case-based reasoning learning. Then, the weighted global similarity measure is used to obtain the set of similar cases from the case database. Finally, a design example of a computer numerical control tool holder product is studied to present the practicability and effectiveness of the proposed method.

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Section: Requirement Analysis Of Intelligent Product Designmentioning

confidence: 99%

Knowledge Service Technology for Supporting Intelligent Product Design

Wang

Niu

2021

Shock and Vibration

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“…In addition, fuzzy set theory is also widely used in attribute data mining. Gupta et al [31] and Sun et al [32] introduced fuzzy weight into fuzzy multi-attribute mining. Meng et al [33] used interval type-2 fuzzy sets to solve multi-attribute decision-making in sponge city construction.…”

Section: Introductionmentioning

confidence: 99%

A Spatial Fuzzy Co-Location Pattern Mining Method Based on Interval Type-2 Fuzzy Sets

Guo

Wang

2022

Applied Sciences

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The goal of spatial co-location pattern mining is to find subsets of spatial features whose instances are often neighbors in a geographical space. In many practical cases, instances of spatial features contain not only spatial location information but also attribute information. Although there have been several studies that use type-1 fuzzy membership functions to mine spatial fuzzy co-location patterns, there is great uncertainty associated with such membership functions. To address this problem, we propose a spatial fuzzy co-location pattern mining method based on interval type-2 fuzzy sets. First, we collect the interval evaluation values of the interval data of attribute information from experts to form granular data. Next, the original type-1 fuzzy membership function is extended to a granular type-2 fuzzy membership function based on elliptic curves. We use a gradual method to adjust the parameters of the fuzzy membership function so that its footprint of uncertainty satisfies both the connectivity and the given confidence. Based on this granular type-2 fuzzy membership function, we fuzzify the attribute information of instances and define the concepts of fuzzy features and fuzzy co-location patterns. A fuzzy co-location pattern mining algorithm based on spatial cliques is then proposed, termed the FCPM-Clique algorithm. In order to improve the efficiency of the algorithm, we propose two pruning strategies. In addition, we extend two classical spatial pattern mining algorithms, the Join-based algorithm and the Joinless algorithm, to mine fuzzy co-location patterns based on interval type-2 fuzzy sets. Many experiments on synthetic and real-world datasets are conducted, the performance of the three algorithms is compared, and the effectiveness and efficiency of our proposed FCPM-Clique algorithm is demonstrated.

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Sustainability evaluation of the provincial water-energy-food nexus in China: Evolutions, obstacles, and response strategies

Qian

Liang

2021

Sustainable Cities and Society

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Multiple Attribute Variable Weight Fuzzy Decision-Making Based on Optimistic Coefficient Method

Cited by 4 publications

References 2 publications

Knowledge Service Technology for Supporting Intelligent Product Design

Knowledge Service Technology for Supporting Intelligent Product Design

A Spatial Fuzzy Co-Location Pattern Mining Method Based on Interval Type-2 Fuzzy Sets

Sustainability evaluation of the provincial water-energy-food nexus in China: Evolutions, obstacles, and response strategies

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