| 摘要: |
| 文章首先分析了传统的实例检索策略的不足,提出了一种 基于模糊相似优先比的混合属性实例的检索模型.该模型用语义距离来刻画两实例对应属性 之间的相似程度,允许实例的属性为模糊数的情形,能胜任定量、定性和混合属性实例的检索 问题. |
| 关键词: 人工智能,基于实例的推理,模糊相似优先比,混合属性实 例检索,语义距离. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家自然科学基金和国家863高科技项目基金资助. |
|
| A Model for Mixed Attributions Cases Indexing |
|
ZHONG Shi-sheng,WANG Zhi-xing,HE Xin-gui
|
| Abstract: |
| In this paper, the disadvantages of the existi ng cases indexing models are analyzed. A new kind of cases indexing model for mi xed attributions cases indexing based on fuzzy analogy preferred ratio is presen ted. This model uses fuzzy distance to describe the similar degree between the c orresponding attributions of two cases and is suitable for numerical, linguistic and mixed attributions cases indexing. |
| Key words: AI(artificial intelligent), CBR(case-ba sed reasoning), fuzzy analogy preferred ratio, mixed attribution cases indexing, fuzzy distance. |
