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Since the stock value is estimated by a fuzzy expression tree which calculates to a fuzzy number, the stock value becomes multi-valued.
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.
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In particular, for the first issue, we propose a new approach that can effectively determine the appropriate referential time for implicit temporal expressions and further present an effective defuzzification algorithm to find the right explanation for a fuzzy temporal expression.
On one hand, to capture linguistic information, Yu [18] employed an absolute term linearization technique and a fuzzy rating expression into a GP-AHP model for solving fuzzy AHP problems.
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.
The considered aspects cover the composite structure, the polymorphic behaviour and the building of a system of fuzzy expressions.
It is a powerful tool for incorporating imprecise data and fuzzy expressions into decision framework (Liu et al. 2014).
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.
This model can handle a complex MCDM problem with a hierarchical structure and use a fuzzy representation for comparative linguistic expressions based on a fuzzy envelope for HFLTS.
Chuang et al. (2009) [ 16] integrated ChIP-chip data, gene expression data and PWM data and developed a fuzzy logic approach called ANFIS to identify potential transcriptional interactions.
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