模糊属性Petri网建模方法及学习模型研究  

Research on Modeling Method and Learning Model of Fuzzy Attribute Petri Net

作　　者：周如旗[1] 冯嘉礼[2] 张谦[1,3] 

机构地区：[1]广东第二师范学院计算机科学系,广州510303 [2]上海海事大学信息工程学院,上海200135 [3]华南理工大学自动化科学与工程学院自主系统与网络控制教育部重点实验室,广州510640

出　　处：《计算机工程》2014年第6期190-194,共5页Computer Engineering

基　　金：国家自然科学基金资助项目(60075016);广东省科技计划基金资助项目(2012B010100049)

摘　　要：定性映射易于表达模糊不确定性知识,但其在表达人类认知思维活动动态特征上存在不足;模糊Petri网比较符合人类思维方式,但相关参数不易获得且其自学习能力存在较大局限性。为此,提出一种模糊属性Petri网(FAPN)形式定义及建模方法。在FAPN结构中构建定性基准参数学习方法,通过定性映射定义4类变迁发生的模糊定性判断规则和相应变迁发生后的结果运算公式,给出FAPN模型的推理算法和学习机制,并模拟系统的动态运行过程。分析结果表明,该方法能有效提高FAPN的学习能力,可适用于以定性判断为特点的诊断系统。The qualitative mapping can be easy to express fuzzy uncertain knowledge, but it is not a good representation method of dynamic characteristics of cognitive thinking action. Fuzzy Petri net is more consistent with human＇s thinking mode, but its parameters are not easy to be obtained and it has limitations in self-learning ability. For these reasons, the formal concept and modeling method of Fuzzy Attribute Petri Net（FAPN） are defined. The learning method about the parameters is constructed in the FAPN structure. Four types of fuzzy qualitative judgment rules and the operation formulas of the transition node are defined based on the qualitative mapping. The reasoning algorithm and the learning method of FAPN are proposed, which can simulate the dynamic process of the network system. Analysis results show that, the proposed method can make FAPN have better learning ability, and it is also useful in the diagnosis system characterized with the qualitative judgement.

关 键 词：模糊属性Petri网 定性映射 定性基准变换 定性判断规则 知识推理 机器学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]