Research On Rapid Knowledge Acquisition Algorithm Of Information System Based On Grc And FCA

Knowledge expression and acquisition is the key issue in artificial intelligence,and the acquisition of rules is one of the important research contents.Granular computing(GrC)is an efficient way,to analyze complex problems.Rough set theory(RST),as one of the most important branches of GrC,is mainly about approximate approximation of uncertain information.It has been described and analyzed the data and then excavates the hidden knowledge in the data to reveal the inherent law.Formal concept analysis(FCA)is a powerful tool for data analysis and rule acquisition based on formal context.FCA is a kind of representation model of knowledge,which mainly depends on the extension and connotation of the formal concept,and establishes the hierarchy based on the partial ordering relationship between formal concepts.RST and FCA are two emerging branches of mathematics in recent years,and they have developed into two important mathematical tools for knowledge acquisition.Although the research methods of RST and FCA are different,their research background and research goal are the same,and the knowledge discovery of complex information system can be carried out.Truth table can be regarded as a special information system.Truth table simplification is widely used in the process of combinational logic circuit optimization.The reduction of large-scale truth table is of great significance to the simplification of logic circuit.This article focuses on the rules of information systems from the perspective of GrC and RST and FCA,mainly from the following aspects:(1)For the existing algorithms for extracting rule information based on the FCA,most researches mainly focus on attribute reduction,but less on rule extraction.Based on this,a simple rule extraction algorithm based on FCA is proposed.The heuristic algorithm is used to speed up the determination of redundant rules.Finally,the simplest decision rules are obtained by removing the redundant attributes.(2)FCA and analyzed by complete information processing system described in the formal context,however,in most cases,the information system is not complete.To deal with this problem,from the perspective of RST,an augmented formal context describing incomplete information system is defined based on concept lattice theory.The polar concept and polar concept lattice are proposed.Meanwhile,the polar concept generation algorithm is proposed.In order to obtain a more concise rule,a new decision rule obtaining algorithm without redundant attributes is proposed.(3)In large-scale logic circuit analysis and design,the process of expressions of the simplest logic functions obtained directly from truth tables is often complicated.To solve this problem,a fast algorithm of large scale truth table reduction based on granularity is proposed.The algorithm simplifies the large-scale truth table by introducing the mark matrix and the heuristic operator to get the simplest logic function expression.(4)For the FCA in the process of rule extraction,there are a lot of redundant concepts.In this paper,FCA is incorporated into the problem of truth table reduction,and the theorem of the most simple rules of truth table is given.At the same time,truth table reduction algorithm based on formal concept analysis is proposed.The algorithm avoids the generation of a large number of redundant concepts to effectively solve the problem of large-scale truth table optimization.