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It is a powerful tool for incorporating imprecise data and fuzzy expressions into decision framework (Liu et al. 2014).
The considered aspects cover the composite structure, the polymorphic behaviour and the building of a system of fuzzy expressions.
This leads to the initial idea of assigning, let's say, statements about the importance or influence to single propositions (e.g., in terms of fuzzy expressions).
Since the data available from rural and unorganised sectors are often ill-defined and subjective in nature, many coefficients are fuzzy numbers, and hence several constraints appear to be fuzzy expressions.
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In our fuzzy GP model the value of a stock is represented as a fuzzy expression tree whose terminal nodes are allowed to be fuzzy numbers.
Since the stock value is estimated by a fuzzy expression tree which calculates to a fuzzy number, the stock value becomes multi-valued.
In order to calculate the expected value of fuzzy expression and search the optimal values of order quantity, reorder point and lead time, a fuzzy simulation algorithm and an iterative algorithm are designed, respectively.
Each has a different fuzzy face, weird nose, and amusing expression.
The fuzzy linguistic expressions of input and output system variants are given in Table 3.
In this framework models take the form of mass relations on joint label set space and can be inferred from data or from fuzzy label expressions.
Mass relations define a probability distribution over the set of fuzzy label expressions but can also be mapped to distributions on the underlying parameter space.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com