A Rule Reasoning Method and Its Application

In this paper we present a rule reasoning method, which is based on rough sets theory, for inducing rules from examples. The key idea of the method is that it combines a criterion of the dependency degree of attributes with decision makers´ priori knowledge to select attributes of objects. Especially, it uses a compound weights algorithm to perform a proper reduction owing to several reductions that each rule can have and select the most effective attribute subset. As a result, a practical and effective reduced knowledge rule set can be acquired. In order to evaluate the effectiveness of the method, we apply the obtained knowledge rule set to optimization control of a prototype simulation system. Experimental results show that the rule reasoning method is more efficient