Expert System Research Based On Rough Set

Expert System is one of the most active and the widest fields of application research in Artificial Intelligence. Expert System is a simply computer program system that simulates human experts to solve certain problems. It endows special domain knowledge of human experts to machines, reasoning and judging in thought activity that computer simulates experts, in order to achieve experts' level in solving problems. Expert system has successful application in many domains. However, with the development of technology and improvement of demands, some problems have appeared such as knowledge acquisition and how to deal with fuzzy problems or uncertainty. At present, the level of expert system cannot meet the needs of practice. Ultimate reason is that there is not breakthrough in the theory methods of system's knowledge express and management The problem that expert system must solve is that how to acquire inexact knowledge and relation, at the basis of which, a correct conclusion must be drawn.Rough sets theory is a new learning hotspot. It can effectively deal with incomplete, uncertain knowledge express and reasoning, its validity has been proved in many fields. Rough sets theory need not any prior knowledge or information, and can analyze and dispose imprecise, inconsistent, incomplete datum. It discloses potential disciples, pick up useful information and reduce information by finding hidden relation among data. Therefore, it has a wide foreground to introduce knowledge acquisition and knowledge reducing in expert system.In this paper a rough-set-based expert system model is proposed. This model introduces a knowledge acquisition filter during the phase of knowledge acquisition. According to the variation of the knowledge dependency, it evaluates and classifies the newly collected knowledge. This system also introduces a mechanism for knowledge reconstruction during building knowledge base to refine and restructure the primitive knowledge base building. This method presented in the paper not only removes the redundant attributes of the knowledge base, but also restructures the value space of the attributes, andimproves the performance of the whole system significantly.Moreover, on the basis of fault diagnosis expert system, rough set theory is introduced. Knowledge representation system table is taken as a major tool to reduce the rules of expert system in which unnecessary properties are eliminated. The redundancy of fault diagnosis information is revealed. The complexity of fault diagnosis expert system's structure is also reduced. The decision-making rules are given finally.