Rough Sets In The System Identification Study

System identification aims to find out a system which is equivalent to the system being identified. System identification is one of the important approaches for system modeling, and modeling method provides a basis for systems analysis, design, control and decision-making. But for some complex systems, building accurate mathematic model is very difficult, so other methods are needed to build approximate models.Alt-hough these approximate models are not accurate, but they can reflect the basic laws of complex systems. Rough set theory which is developing rapidly in recent years provides such a tool.Without any prior knowledge, only based on the information provided in the data itself, Rough set theory can be used for data analysis and pattern recognition to find useful information hidden in the data .Therefore , it can be used for system identification and system analysis. Based on rough sets, this paper will mainly discuss system identification which includes rule acquisition and fuzzy system modeling. Here, rule acquisition intends to obtain the rules which reflect the input-output characteristics of complex systems. System modeling aims to construct a rule-based system to simulate the input-output characteristics of the complex system.First of all, some basic concepts, such as rough sets, decision table, decision rule and decision rule base, are given concisely. All these make a great theoretical preparation for the following applications. Then, in the third chapter, rule acquisition and the algorithm are discussed firstly. So the rule base (knowledge base) which reflects the input-output regulation can be obtained. But in this rule base, there maybe some inconsistent rules which will cause great trouble in our application. So in order to make these inconsistent rules consistent, the probability properties of basic rule base arediscu-ssed firstly, and then, the concepts of minimum-loss rule base and minimum-error-rate rule base are proposed innovatively. Through the discussion, the corresponding methods of consistent rule acquisition and the error-rate are obtained. Further more, in the fourth chapter, one method for fuzzy system modeling is discussed and its deficiency is analyzed firstly. In order to resolve its deficiency, a new method for fuzzy system modeling based on rough set is proposed. Based on the powerful ability of rough set on attribute reduction and rule acquisition, the fuzzy rules can be obtained automatically from the data. On the basis of the fuzzy rule base, a fuzzy system is designed. In chapter 4, the discussed method for system identification is used in the area of time series prediction, and simulating results indicates that this method is efficient.