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Both children are selected with a specific membership degree.
Concretely speaking, this paper considers that the membership degree and the non-membership degree of PFSs are expressed as hesitant fuzzy elements.
The former applies to qualitative single-factor membership degree, such as fault properties, while the latter applies to quantitative single-factor membership degree, such as fault plane pressure.
Especially, the membership degree of the pixel located at the peak is 1, while the membership degree of the pixel located at the trough is 0.
The risks of the risk factors are evaluated by fuzzy membership degree.
Proposed approach reports the risk class and its membership degree such as Minor (0.7).
Then according to the principle of maximum membership degree, the paper selects the most suitable engine.
It evaluates the change of membership degree and judges things from multiple indexes.
In this paper, the dynamic clustering method was introduced to determine the single-factor membership degree.
After the calculation, the connected region is sorted into the highest membership degree.
The traversing procedure starts with setting the membership degree of the root to 1.
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