| In 1982, Rough set theory proposed by Z.Pawlak is a tool which can deal with integrity and uncertainty mathematical. It can analysis and deal with Various kinds of implicit information which is inaccurate, consistent and incomplete and etc and reveal the potential law. The main idea is finding the problems decision or classification rules by knowledge reduction in the condition of keeping the classification ability stable and getting extremely extensive application in pattern recognition, machine learning and data mining and etc. The best way to solve the above problems by Z-rough set is rule extract. Z-rough set is a set of static characteristics. Therefore its application is very limited and a new method is demanded urgently, especially for volatile financial system, investment system, intelligent system, target changeful recognition-analysis system, dynamic data mining, dynamic knowledge discovery. In 2002, Shi Kaiquan, the professor of Shandong university put forward S-rough set rough theory. He developed rough set theory from the static promotion to dynamic and put forward the thoughts of rule mining. S-rough set is the extension of Z-rough which is a foundation of S-rough set.This article support a new theory and thought for the law recognition of the protean financial system, risk investment system, state identification system, fault diagnosis system and so on and law generation and law mining of system state. This article makes a further discussing about the structure and application of S-rough sets and function S-rough set, gives some research on rule mining and find dynamic law by interpolation generation function. At the same time, some examples show the application of S-rough set in data hierarchical mining of network data and medical diagnosis system. Finally, this note also does some research on the output feedback of nonlinear systems realizing a class of nonlinear system finite-time stabilization and output tracking. |
PDF Full Text Request