粗糙集理论的分层递阶约简算法及其信息理论基础

本文模拟人类认知的分层递阶原则,提出一种粗糙集理论的分层递阶约简算法.该算法首先将信息系统或决策系统的知识在由部分属性所构成的多种层次和多种粒度上表示出来,然后分别对各个属性层次进行递阶约简.因此,该算法具有较强的实用性和较好的动态特性,并且能并行运算.同时,本文从信息理论的角度证明了分层递阶约简的理论基础.文章的最后将该算法应用于某水泥窑炉控制决策的获取中,证实了其有效性.

Hierarchical reduction approach of rough sets theory and its basis on the information theory

Simulating the hierarchical principle of human cognizance process, a hierarchical reduction algorithm of rough set theory is proposed in this paper. In the algorithm, the attributes are firstly partitioned into different parts allocated at several layers. Then the knowledge in the information systems or decision systems is presented hierarchically with multiple granularities at multiple layers, and the reduction is hierarchically applied to parts of attributes allocated at each layer. The algorithm has a strong applicability and a good dynamic property, and it can be implemented in parallel. At the same time, the foundation of the algorithm is justified on the basis of information theory. Finally, the hierarchical reduction algorithm is used to acquire the control decision of a cement kiln, which verifies the effectiveness of the algorithm.