| 嵌入模糊逻辑的上下文存储与查询机制 |
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| 引用本文： | 叶剑,李锦涛,高晓芳,朱珍民,刘金刚.嵌入模糊逻辑的上下文存储与查询机制[J].软件学报,2010,21(Z1):12-20. |
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| 作者姓名： | 叶剑 李锦涛 高晓芳 朱珍民 刘金刚 |
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| 作者单位： | 中国科学院 计算技术研究所,北京 100190; 中国科学院 研究生院,北京 100049;中国科学院 计算技术研究所,北京 100190;中国科学院 计算技术研究所,北京 100190; 首都师范大学 计算机科学联合研究院,北京 100037;中国科学院 计算技术研究所,北京 100190;首都师范大学 计算机科学联合研究院,北京 100037 |
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| 基金项目： | Supported by the National High-Tech Research and Development Plan of China under Grant No.2009AA011906 (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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| 摘 要： | 上下文存储和查询是上下文感知计算的基础.而上下文的不确定性是上下文处理中不可回避的问题.为此,提出了一种嵌入模糊逻辑的上下文感知系统架构(FLECA).FLECA 将基本上下文信息转换成高层上下文语义,通过内嵌模糊逻辑引擎,实现对模糊上下文信息的全模糊存储和查询;同时基于神经网络,实现查询推理机制的学习和完善.实验表明引入学习机制能够有效提高FLECA 的适应性,从而为以用户为中心的应用提供有力的支撑.
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| 关 键 词： | 上下文感知 模糊逻辑 神经网络 SPARQL |
| 收稿时间： | 2009/7/15 0:00:00 |
| 修稿时间： | 7/9/2010 12:00:00 AM |
Embedding Fuzzy Logic in Context Storing and Querying |
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| YE Jian,LI Jin-Tao,GAO Xiao-Fang,ZHU Zhen-Min and LIU Jin-Gang.Embedding Fuzzy Logic in Context Storing and Querying[J].Journal of Software,2010,21(Z1):12-20. |
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| Authors: | YE Jian LI Jin-Tao GAO Xiao-Fang ZHU Zhen-Min and LIU Jin-Gang |
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| Affiliation: | Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100190, China; Graduate School, The Chinese Academy of Sciences, Beijing 100049, China;Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100190, China;Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100190, China; Joint Faculty of Computer Scientific Research, Capital Normal University, Beijing 100037, China;Institute of Computing Technology, The Chinese Academy of Sciences, Beijing 100190, China;Joint Faculty of Computer Scientific Research, Capital Normal University, Beijing 100037, China |
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| Abstract: | The storage and query of context is the foundation of context awareness based computing. At the same time, uncertainty of context is an inevitable problem of the context awareness. Therefore, a fuzzy logic embedded context-aware architecture (FLECA) is proposed in this paper. FLECA gets the high level context semantic from querying those primary contexts. During the procedure of management of contexts, fuzzy logic engine enables FLECA to store and query the fuzzificated contexts. Furthermore, a neural network based learning machine is introduced into the management of fuzzy rules. The experiment shows that the learning machine is able to improve the adaptability of FLECA to provide stronger support to the user-centric applications. |
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| Keywords: | context awareness fuzzy logic Neural Network SPARQL |
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