粗糙控制可调精度规则挖掘模型与应用

Adjustable precision of rule mining model in rough control and its application

针对粗糙控制中控制精度不高、控制效率低等问题,提出了基于区间概念格的粗糙控制可调精度规则挖掘模型,以降低规则挖掘成本及提高应用效率.模型首先对采集到的原始数据进行预处理,使其转化成布尔型的形式背景;其次,参照一般区间概念格的生成算法进行了粗糙控制背景下的区间概念格的构造;再次,提出可调精度控制规则挖掘算法,建立了基于区间概念格的粗糙控制可调精度规则挖掘模型,最后,模型分析和实例对比验证了模型在提高规则可靠性的前提下,实现了挖掘成本和控制效率的最优化.

According to the problems of low accuracy, efficiency in rough control, this paper put forward a adjustable precision of rule mining model in rough control based on concept lattice, in order to reduce the cost and improve the efficiency of rule mining application. Firstly, the model preprocess the collected original data, making it transformed into boolean formal context; secondly, referring to the generation algorithm of interval concept lattice, this study will structure interval concept lattice under the background of rough control; again, putting forward the adjustable precision control rule mining algorithm, the rough control adjustable precision rules mining model will be established based on interval concept lattice; finally, in the premise of improving the reliability of the rules, model analysis and contrast examples validate the optimization of mining cost and control efficiency.