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大多用语言描述偏好的决策问题假设语言是均匀、对称分布的,然而有些问题需要采用非均衡语言.针对这一问题,提出一种基于符号化方法的语言计算模型.首先,构造一种基于基础语言集合的加权图,用图形中的点描述非均衡语言;然后,定义图形中任意两点间的曼哈顿距离公式,用于计算非均衡语言的距离;最后,将其用于逼近于理想值的排序方法(TOPSIS),并给出算例.所提出的方法不仅图像化非均衡语言,而且在解决TOPSIS问题时比欧氏距离测度更具优越性.
Most of the language used to describe the preference decision problem hypothesis language is uniform, symmetrical distribution, but some problems need to use non-equilibrium language.Aiming at this problem, this paper proposes a language computing model based on symbolic method.First, to construct a based on the basis of Weighted graph of language set describes the unbalanced language by the dots in the graph. Then, the Manhattan distance formula between any two points in the graph is defined to calculate the distance of the unbalanced language. Finally, it is used to approximate the ideal value Order method (TOPSIS) is given and an example is given.The proposed method not only visualizes unbalanced language, but also has more superiority than Euclidean distance measure in solving TOPSIS problem.