Attribute Approach Based On Rough Set Theory And Fuzzy Rule Extraction And Application Of Research

Rough Sets theory is a tool for effectively analyzing various kinds of the imprecise, inconsistent and incomplete information, which can analyze and reason the data, discover the connotative data and find out the latent rules. In 1982, Z.pawlak published his paper-Rough Sets theory , which declared the beginning of the Rough Sets. After that, much research has taken on the side of theory and application, it accelerates the application of the Rough Sets in the various field. But the theory is incomplete, it cann't meet the need for solving the practical problem. Two approach based on Rough Sets defect for disposing attributes and a new algorithm for generating rules which combines Rough Sets theory and Fuzzy theory are presented in this paper. In the end, the paper discusses the application in the digital circuit.Chapter one general introduction. This paper begins with reviewing the origin and development of Rough Sets, and then treats with its recent studies from the respect of theory and application. At last, the main studies of this paper are described.Chapter two the fundaments of Rough Sets theory. Here we discuss the formalized definition of knowledge and some properties; the relation of the knowledge and classification; Rough Sets; the positive region, the negative region and the boundary region; the upper and lower sets and its properties; inaccuracy mathematics properties; class approximation and sets rough equivalence.Chapter three the reduction of the knowledge representation system and the rule generation. In this chapter, we illustrate the formalized definition of the knowledge representation system and the decision table; some properties of the decision table attributes. We give several solutions to the attributes reduction and rules generation. We also discuss the rules with the rough operator, the variable precision Rough Sets model and its incremental learning algorithm.Chapter four the attributes disposing, fuzzy rules generation and RS application. We present two approach on dealing with attributes and propose a rule generation algorithm which combines Rough Sets and Fuzzy tbeozy In the end, we discuss the application based on RS in the digital circuit.Chapter five conclusion. This chapter gives conclusion of this paper and simply describes the new ideas of this paper. At last, some problems in this paper that need to be improved on are proposed.