摘要
模糊综合评判在不能界定隶属度转换是线性转换条件下,硬性用加权平均线性模型实现隶属度转换.通过揭示指标隶属度中通常包含确定目标隶属度不起作用的非线性冗余值,建立区分权滤波的冗余理论,用冗余理论界定隶属度转换不是线性转换,并构建近似推理逻辑支撑的隶属度转换模型.阐述所建模型为什么是当前条件下人们能构建的最优近似模型.当指标隶属度只取1或0两个值时模型将退化成模糊综合评判的"加权平均"模型.
The fuzzy comprehensive evaluation uses weighted average linear model to achieve membership transformation without consider weather the transformation is linear or not. According to reveal the nonlinear residual value which hidden in index membership degree, the residual theory based on distinguishable weight filter is established. And then it is explained that the membership transformation is not the linear transformation. The membership transformation model based on approximate reasoning logic is built. In this article, we explain why the model is the best approximation model that people can construct under the current conditions. The model will be the weighted mean model when the index membership degree is only 1 or 0 two values.
引文
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