摘要
模糊Petri网(fuzzy Petri nets,FPN)是基于模糊产生式规则的知识库系统的有力建模工具,但其缺乏较强的自学习能力。在FPN的基础上引入神经网络技术,给出了一种自适应模糊Petri网(adapt fuzzy Petri nets,AFPN)模型。该模型将神经网络中的BP网络算法引入到FPN模型中,对FPN中的权值进行反复的学习训练,避免了依靠人工经验设置带来的不确定性。AFPN具有很强的推理能力和自适应能力,对知识库系统的建立、更新和维护有着重要的意义。
Fuzzy Petri nets（FPN） is a powerful modeling tool for knowledge-based systems based on fuzzy production rules, but it lacks of strong learning ability. Based on the FPN, imported the artificial neural networks, this paper presented an adaptive fuzzy Petri nets（ AFPN ） model. So introduced the back propagation algorithm of neural networks into FPN. The AFPN has powerful reasoning ability and adaptive ability. It has important significance to build, update and maintenance the knowledge
出处
《计算机应用研究》
CSCD
北大核心
2009年第10期3801-3803,共3页
Application Research of Computers
基金
国家自然科学基金资助项目(60702053)
