| 摘要: |
| 自适应模糊Petri 网兼具模糊Petri 网的模糊推理能力和神经网络的学习能力,是普适计算的模糊情境推理机制的重要的形式化规约工具.但该模型依赖于离线训练数据集,无法适应动态变化的普适智能环境.在自适应学习Petri 网模型中嵌入反馈机制,并对将模糊逻辑引入对上下文的表示,利用神经网络的反向传播学习方法对隶属度函数的参数进行学习,提高了模型的场景适配和个性化自学习能力.通过设计服务推荐系统,建立了模型仿真与验证实验环境.实验结果表明,该方法可以有效提高系统学习能力,进而提高推理正确率. |
| 关键词: 模糊推理规则 模糊逻辑 人工神经网络 反向传播算法 |
| DOI： |
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| 基金项目:Supported by the National High-Tech Research and Development Plan of China under Grant No.2009AA011902 (国家高技术研究发展计划(863)); the National Natural Science Foundation of China under Grant No.61070109 (国家自然科学基金) |
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| A Feedback Adaptive Fuzzy Petri Net Model for Context Reasoning |
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WEN Sai-Ping, ZHU Zhen-Min, YE Jian
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Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100190, China
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| Abstract: |
| As an improved model of fuzzy Petri net, adaptive Petri net (AFPN) has got the learning ability from neural network. But AFPN still depends on offline training data, while actual environment is so complex, vague and changeful that AFPN seems slightly inadequate. This paper proposes an approach based on fuzzy logic and feedback theory to improve AFPN. The approach introduces feedback mechanisms into AFPN to enhance the adaptive ability in dynamic environment. In addition, the approach embeds fuzzy logic theory into the representation of context information. Thus, the uncertain context information management is more conformable with person’s sense. The approach is also able to learn the parameters of membership function by using the back propagation algorithm of neural network. At the end of the paper, an experiment is designed to demonstrate that the approach is feasible and effective in fuzzy reasoning. |
| Key words: fuzzy inference rule fuzzy logic neural network back propagation |
