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

M. Prasad, Kuang-Pen Chou, A. Saxena, Omprakash Kaiwartya, Dong-Lin Li, Chin-Teng Lin
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引用次数: 11

Abstract

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.
具有聚类中心映射的Mamdani型模糊推理系统的协同模糊规则学习
利用协同模糊聚类的知识学习能力和FCM的规则学习能力,提出了一种新的Mamdani型模糊推理系统模型。协作过程寻找不同数据集之间的一致性,这些数据集可以在不同的地方或相同的地方产生,不同的环境包含共同的特征空间,并汇集在一起寻找其中的共同特征。对于任何类型的数据集协作或集成,都需要在某种程度上保持隐私和安全。通过使用协作过程,模糊推理系统可以准确地定义用于结构学习的规则数量,在保证给定数据集的隐私性和安全性的同时,使系统的性能保持在令人满意的水平。
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