摘要
讨论了模糊属性自动机对由模糊属性正则文法产生的字符串进行识别的问题,建立了以标准字符串为核的隶属函数,利用该函数求出待识别串在标准串中各字符的隶属度,进而得到各字符与标准字符的差别,由此可求出待识别串与标准串差异最大的标准字符.进而对多个串的识别,利用格比较得到与标准串最相符的待识别串,从而提高了字符串的识别率和识别效果.
In this paper, the concepts of fuzzy attributive regular grammar and fuzzy attributive automata are introduced. In recognizing process, we give a fuzzy membership function whose core is the known strings. The difference between known strings and unrecognized strings is depicted by the resulting of the fuzzy membership function. Therefore, the most different word of unrecognized strings can be found. If there are more resulting of recognizing process, we would apply space comparison to gain the most identical string with known strings. So the resulting of recognizing process is more effective and efficient.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2004年第4期396-398,共3页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.60074014)
关键词
句法模式识别
隶属度
模糊属性文法
模糊属性自动机
欧几里德贴近度
Syntactic Pattern Recognition
Degree of Membership
Fuzzy Attributive Regular Grammar
Fuzzy Attributive Automata
Euclidean Near Degree
