基于最邻近法的注塑产品设计事例检索策略

K-Nearest Neighbors-based case retrieve strategy for injection product design

针对注塑产品特点,提出了基于粗糙集和模拟退火算法的事例最邻近检索策略。基于粗糙集对事例属性进行约简,获得了事例属性的重要度排序,以此建立了事例库的层次聚类模型和分配特征属性权重值。根据约简后的结果,将事例库组织成层次结构,可有效缩小事例搜索空间,提高检索效率。基于模拟退火算法对特征属性的权重值进行优化,通过在相似度检索模型中加入权值分配的指导信息,防止检索出的相似度系数最大的事例并非最佳事例,提高了检索的质量。应用实例表明,该方法能够有效提高基于事例的推理系统整体的检索效率与质量。

According to the characteristics of injection products, a K- Nearest Neighbors （K- NN） case retrieval strategy was proposed based on rough set and simulated annealing algorithm. Through use of rough set, case attributes were reduced and the importance sequence for attributes was obtained, which was employed to build case hierarchy clustering model and allocate attributes weights. With reduction results, case base was organized as a hierarchical structure therefore the case retrieval space was reduced and the retrieval efficiency was improved. By using of simulated annealing algorithm, attribute weights of hierarchy clustering model were optimized in terms of weights allocation quotas, which was used to improve the case retrieval efficiency and quality. The K NN case retrieval strategy has been applied in injection mould design system which indicated that the strategy could dramatically improved overall case retrieval efficiency and quality in case-based reasoning system.