基于模糊传感器的实例信息聚合

G. Mauris, E. Benoit, L. Foulloy
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引用次数: 6

摘要

互补信息的聚合问题是大型智能系统监控中的一个关键问题。本文处理的情况是,我们没有一个分析的数学模型来获得新的信息,也没有一个基于规则的模型可供我们使用,但只有几个例子,以语言的方式表达的基本模糊传感器。我们在模糊子集理论的框架内解释了数字-语言转换是如何在称为模糊传感器的传感器内进行的。接下来,我们提出了一种从所研究的示例中提取规则的方法,以及如何以这种方式从温度和湿度的语言测量中获得舒适度的语言描述。最后,通过实例指出了我们的方法的优点,以及改进语言建模过程的发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The aggregation of information by examples via fuzzy sensors
The problem of the aggregation of complementary information is a crucial point in the monitoring of large intelligent systems. The paper deals with the cases in which we do not have an analytical mathematic model to derive new information, nor a rule-based model at our disposal, but only a few examples expressed in a linguistic manner by basic fuzzy sensors. We explain within the frame of the fuzzy subset theory how the numeric-linguistic conversion is carried out inside a sensor, called a fuzzy sensor. Next, we present a method to extract rules from the examples studied, and how to obtain in this way a linguistic description of comfort from linguistic measurements of temperature and humidity. Finally, the advantages of our approach are pointed, out as well as the developments to come to improve the process of linguistic modelling from examples.<>
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