摘要
为了提高高速公路应急预案匹配的智能性和实时性,引入了人工智能领域中的范例推理技术(CBR)并提出一种模糊匹配方法作为其检索算法。该方法结合最大隶属度原则确定突发事件和案例库中各个案例定量描述属性所属的模糊集,利用格贴近度计算突发事件与案例对应各属性之间的相似程度,结合权重选择出相似度最大的案例作为预案。通过与现有匹配算法的对比,体现出模糊匹配算法能有效的处理模糊性而在交通应急预案匹配中的优越性,是一种可行的应急预案智能匹配方法。
In order to improve the inteI igence and reaI-time of traffic emergency prepIan matching,this paper presents case-based reasoning technoIogy in the fieId of artificiaI inteI igence and proposes a fuzzy matching method as its search aIgo-rithm.In the method,quantitative attributes of emergency and cases are identified to fuzzy sets based on the principIe of maximum degree of membership.The cIose degree between emergency and cases can be caIcuIated with weights and simiIarity between attributes.By comparison with the existing matching aIgorithms,it reveaIs the superiority of fuzzy matching on traffic emergency prepIan matching because of deaIing with ambiguity effectiveIy.
出处
《工业控制计算机》
2015年第1期133-136,共4页
Industrial Control Computer
基金
江苏省交通科学研究项目(7650146044):江苏省高速公路交通紧急救援决策支持系统研究
关键词
范例推理
模糊匹配
模糊集
比较
case-based reasoning
fuzzy matching
fuzzy set
comparison
