Fuzzy-neural theory applied to Web-based proactive service

Yuan-chao Li, Xiao-dong Wu, Degan Zhang, Guangping Zeng
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引用次数: 1

Abstract

In order to realize proactive service, we design and improve relative fuzzy-neural 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 sort fuzzification in this paper. For proactive decision, fusion method based on fuzzy-neural can make Web-based intelligent 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.
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模糊神经理论在基于web的主动服务中的应用
为了实现主动服务,设计并改进了相关模糊神经算法。一般来说,网络可以分为两类。一是神经系统的模糊逻辑推理是通过模糊权值来完成的。另一种是输入数据必须在第一或第二级模糊化,而不是权重模糊化。本文对第二类模糊化问题进行了讨论和研究。对于主动决策,基于模糊神经网络的融合方法可以使基于web的智能系统保持模糊逻辑系统的优势,并在主动/关注服务中保持自适应最优。我们的新方法的正确性和有效性已经得到了检验。
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