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Collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster centers

机译:基于聚类中心映射的Mamdani型模糊推理系统的协同模糊规则学习

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This paper demonstrates a novel model for Mamdani type fuzzy inference system by using the knowledge learning ability of collaborative fuzzy clustering and rule learning capability of FCM. The collaboration process finds consistency between different datasets, these datasets can be generated at various places or same place with diverse environment containing common features space and bring together to find common features within them. For any kind of collaboration or integration of datasets, there is a need of keeping privacy and security at some level. By using collaboration process, it helps fuzzy inference system to define the accurate numbers of rules for structure learning and keeps the performance of system at satisfactory level while preserving the privacy and security of given datasets.
机译:本文利用协同模糊聚类的知识学习能力和FCM的规则学习能力,提出了一种Mamdani型模糊推理系统的模型。协作过程会发现不同数据集之间的一致性,这些数据集可以在包含公共要素空间的不同环境中的不同位置或同一位置生成,并聚集在一起以在其中找到公共要素。对于任何形式的数据集协作或集成,都需要将隐私和安全性保持在一定水平。通过使用协作过程,它可以帮助模糊推理系统定义用于结构学习的准确规则数,并在保持给定数据集的隐私性和安全性的同时,将系统性能保持在令人满意的水平。

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