模糊诊断规则自学习中规则条件优选技术研究  

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

作　　者：齐怡[1] 沈士团[1] 李驿华[1] 

机构地区：[1]北京航空航天大学电子信息工程学院,北京100083

出　　处：《北京航空航天大学学报》2004年第6期506-511,共6页Journal of Beijing University of Aeronautics and Astronautics

摘　　要：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 .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 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 selected for rule conditions or test points based on the evaluated matrix was designed. The computer simulation shows that the number of rule conditions, which has to be learned, is decreased and the workload of learning is reduced.

关 键 词：专家系统 学习机 模糊规则 条件优选技术 测点优选技术 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP182[自动化与计算机技术—控制科学与工程]