摘要
针对实例推理(CBR)系统中实例无限增加会导致实例库冗余和实例检索效率降低,从而使系统总体性能下降的问题,对现有的实例库维护方法进行了分析,给出了基于实例覆盖性的实例保存策略,以此来限制实例库的无限扩大,进而提出以实例适应度函数对实例在实例库中的存在性进行描述,精简实例库中适应性差的实例,从而优化了实例库的存储结构,提高了系统性能.最后以冲裁模具实例库的维护为例,验证了上述方法的合理性和有效性.
In a Case-Based Reasoning （CBR） system, the case learning makes the case base expand quickly, which results in the redundancy of the case base and the decrease of the searching efficiency. Thus, the system function is weakened. To solve this problem, this paper analyzes the traditional methods of case base maintenance and proposes a case-saving strategy based on case cover capability to limit the infinite expanding of the case base. Moreover, a case fitness function is defined to describe the subsistence of a case in the base, by which some cases with small fitness degree are deleted. Therefore, the storage structure of the case base is optimized and the system performance is improved. A CAD system of punch die design is finally presented to verify the correctness and the rationality of the proposed method.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2005年第12期61-65,共5页
Journal of South China University of Technology(Natural Science Edition)
基金
国家"863"高技术研究发展计划项目(2002A-A411030)
关键词
实例推理
实例库维护
实例覆盖性
实例适应度
冲裁模具
case-based reasoning
case base maintenance
case cover capability
case fitness degree
punch die
