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A Kind of Context-Aware Approach Based on Fuzzy-Neural for Proactive Service of Pervasive Computing

机译:一种基于模糊神经网络的基于模糊性普及计算的一种情境感知方法

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The task-oriented proactive seamless migration is one of difficult problems to be solved in pervasive computing paradigm. Apparently, this function of seamless mobility is suitable for mobile services, such as mobile Web-based learning. But when seamless migration for computing task of learning is realized among PC, laptop, or PDA, there are several difficult problems to be solved, such as how to supply the proactive/attentive service with uncertainty for aware context. In order to realize E-learning based on proactive seamless migration, we design and improve relative fuzzy-neural approach (of course, besides it, there are other approaches). Generally, the network can be classified into two. One is that fuzzy logic reasoning is completed by fuzzy weight in neural system. The other is that the input data must be fuzzified in the first or second level, but not weight. We discuss and study the second in this paper. For proactive decision, fusion method based on fuzzy-neural can make Web-based learning system keep advantage of fuzzy logic system and remain adaptive optimum in proactive/attentive service. The correctness and validity of our new approach have been tested.
机译:面向任务的主动无缝迁移是在普遍计算范例中解决的难题之一。显然,这种无缝移动性的功能适用于移动服务,例如基于移动网络的学习。但是,当在PC,笔记本电脑或PDA中实现学习任务的无缝迁移时,有几个难以解决的问题,例如如何提供具有不确定性的主动/细心服务以了解环境。为了实现基于主动无缝迁移的电子学习,我们设计和改进相对模糊神经方法(当然,除此之外,还有其他方法)。通常,网络可以分为两个。一个是通过神经系统中的模糊重量完成模糊逻辑推理。另一个是输入数据必须在第一或第二级中模糊,但不重量。我们讨论并研究了本文的第二个。对于主动决定,基于模糊神经网络的融合方法可以使基于Web的学习系统保持模糊逻辑系统的优势,并保持主动/细心服务中的自适应最佳。已经测试了我们新方法的正确性和有效性。

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