模糊诊断规则自学习中规则条件优选技术研究

规则条件优选技术是模糊诊断规则自学习方法的重要环节之一.针对条件优选问题,提出了一种基于面积计算的模糊贴近度函数,并采用该函数对各规则条件的可区分程度做出评估,然后根据得到的评估矩阵设计了规则条件和测点的优选算法.仿真证明,通过条件优选可以大量减少规则中条件的数量,减少了规则学习的计算量,提高了学习的效率;同时,所研究的测点优选技术还可为自动测试程序的设计提供参考.

Study for the best selection of rule conditions in automated extraction of fuzzy diagnostic rules

In the machine learning of fuzzy rules for the diagnostic expert systems,the best selection of rule conditions is one of the most important steps. A new fuzzy nearness function was proposed,and the overlap degree of the rule conditions was evaluated to get an evaluated matrix with this function. An algorithm of the best selection for rule conditions or test points based on the evaluated matrix was designed. The simulation shows that the number of rule conditions,which has to be learned,is decreased largely,consequently,the workload of learning is reduced too. Further more,the method of the best selection of test points can do well to the design of automatic test.