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Today, we bring you a code replica of the intensive reading and replication paper "A Linguistic Multi-attribute Decision Making Method Based on CWPHM Operators and C-DEMTEL" (18).
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Today, the editor brings the "intensive reading replica paper 'A linguistic Multi-Attribute Decision-Making approach based on the CWPHM operator and C-DEMATEL' for code reproduction (18)".
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一、内容摘要（Content Summary）
In this issue, we will introduce the code fork of the intensive reading and forking paper "Language-based Multi-attribute Decision Making Method Based on CWPHM Operator and C-DEMATEL" from three aspects: mind map, intensive reading content, and knowledge supplementation (18).
In this issue, we will introduce the code replication (18) of the refined reading paper "Language Type Multi-Attribute Decision-Making Method Based on CWPHM Operator and C-DEMATEL" from three aspects: mind map, refined reading content, and supplementary knowledge.
二、思维导图（Mind Mapping）
三、精读内容（Detailed Reading Content）
According to the references of CWAA operators in this paper, you can obtain the definition and example process of CWAA operators, as shown in the following figure. The CWAA operator is based on the weighted addition of the expectation, entropy, and superentropy of the cloud to obtain the eigenvalues of the aggregated information.
Based on the references in the paper, the definition and example process of the CWAA operator can be obtained, as shown in the figure below. The CWAA operator is a weighted aggregation of expectations, entropy, and hyperentropy of clouds in a certain way to obtain characteristic values of aggregated information.
The following figure shows the code and calculation results of using the CWAA operator to assemble the information of each cadre.
The code and calculation results of using the CWAA operator for the information aggregation of each cadre are shown in the figure below.
四、知识补充——云逆向发生器（Supplementary Knowledge — Cloud Reverse Generator）
If it is necessary to transform the quantitative concept into a qualitative concept, the reverse cloud generation algorithm can be used, that is, the reverse process of the forward operation algorithm, and three numeric eigenvalues can be obtained from the given cloud droplet sample according to the reverse cloud generation algorithm, so as to realize the qualitative evaluation of the sample data, as shown in the following figure. According to this idea, the point estimate of the cloud can be obtained from the sample.
If it is necessary to transform quantitative concepts into qualitative concepts, the reverse cloud generation algorithm can be used, which is the reverse process of the forward operation method. Three numerical characteristic values can be obtained from the given cloud droplet samples based on the reverse cloud generation algorithm, thus achieving qualitative evaluation of sample data, as shown in the figure below. Following this approach, the point estimate of the cloud can be obtained based on the sample.
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That's all for today's sharing.
If you have a unique idea about the article,
please leave us a message,
and let us meet tomorrow.
I wish you a nice day!
References: ChatGPT, Baidu Encyclopedia
Bibliography:
Wang Weiming, Xu Haiyan, Zhu Jianjun et al. Language-based multi-attribute decision-making method based on CWPHM operator and C-DEMATEL [J/OL]. CMS, 2023, 1(1): 1-13.
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