Deep View-Reduction TSK Fuzzy System: A Case Study on Epileptic EEG Signals Detection

Ziyuan Zhou, Yuanpeng Zhang, Yizhang Jiang
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引用次数: 2

Abstract

In many practical applications, the fuzzy systems have been used due to the promising approximation accuracy and the high interpretability. Here, we proposed a novel multiview Takagi-Sugeno-Kang (TSK) fuzzy system in which a deep structure associating with a view-reduction mechanism are involved. The deep structure of each view is constructed by many basic components, i.e., the classic one-order TSK fuzzy systems which are linked in a layer by layer way using the stacked generalization principle. The view-reduction mechanism contains two parts: 1) A user-free parameter which is fixed according to the feature distribution is introduced to guild the view weight learning; 2) Views with noisy weights are automatically filtered by a reduction principle which is generated according to the training data. The proposed multi-view fuzzy system is finally applied for epileptic EEG signals detection.
深度视域约简TSK模糊系统:以癫痫脑电信号检测为例
在许多实际应用中,模糊系统由于具有良好的近似精度和较高的可解释性而得到了广泛的应用。在此,我们提出了一种新的多视图Takagi-Sugeno-Kang (TSK)模糊系统,其中涉及与视图约简机制相关的深层结构。每个视图的深层结构都是由许多基本组件构成的,即经典的一阶TSK模糊系统,这些模糊系统采用堆叠泛化原理逐层连接。视图约简机制包括两部分:1)引入一个根据特征分布固定的无用户参数来指导视图权值学习;2)根据训练数据生成的约简原则自动过滤带有噪声权重的视图。最后将所提出的多视图模糊系统应用于癫痫病脑电信号的检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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