基于模糊子空间聚类的〇阶L2型TSK模糊系统

邓赵红; 张江滨; 蒋亦樟; 史荧中; 王士同

doi:10.11999/JEIT150074

基于模糊子空间聚类的〇阶L2型TSK模糊系统

doi: 10.11999/JEIT150074

基金项目:

国家自然科学基金(61170122)，江苏省杰出青年基金(BK20140001)和新世纪优秀人才支持计划(NCET120882)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-01-13
- 修回日期: 2015-05-11
- 刊出日期: 2015-09-19

Fuzzy Subspace Clustering Based Zero-order L2-norm TSK Fuzzy System

摘要

摘要: 经典数据驱动型TSK(Takagi-Sugeno-Kang)模糊系统在获取模糊规则时，会考虑数据的所有特征空间，其带来一个重要缺陷：如果数据的特征空间维数过高，则系统获取的模糊规则繁杂，使系统复杂度增加而导致解释性下降。该文针对此缺陷，探讨了一种基于模糊子空间聚类的〇阶L2型TSK模糊系统(Fuzzy Subspace Clustering based zero-order L2- norm TSK Fuzzy System, FSC-0-L2-TSK-FS)构建新方法。新方法构建的模糊系统不仅能缩减模糊规则前件的特征空间，而且获取的模糊规则可对应于不同的特征子空间，从而具有更接近人类思维的推理机制。模拟和真实数据集上的建模结果表明，新方法增强了面对高维数据所建模型的解释性，同时所建模型得到了较之于一些经典方法更好或可比较的泛化性能。
- Takagi-Sugeno-Kang(TSK)模糊系统 /
- 医疗诊断 /
- 解释性 /
- 高维数据
Abstract: The classical data driven Takagi-Sugeno-Kang (TSK) fuzzy system considers all the features of trained data, and faces a challenge that the interpretation is degenerated and the obtained fuzzy rule is complex when trained by high dimensional data. In this paper, a new fuzzy model, i.e., Fuzzy Subspace Clustering based zero-order L2-norm TSK Fuzzy System (FSC-0-L2-TSK-FS) is proposed to overcome this difficulty. The proposed fuzzy system not only reduces the feature spaces of the rule of antecedent, but also makes different rules implement the inference in different subspaces. The inference mechanism of the proposed fuzzy model training algorithm is very similar to the inference procedure of human. The experimental studies on the synthetic and real datasets prove that the interpretation of model constructed by the proposed method is enhanced when trained by high dimensional data and the generalization performance is better or comparative to several classical TSK fuzzy systems training methods.
- Takagi-Sugeno-Kang (TSK) fuzzy system /
- Medical diagnosis /
- Interpretability /
- High-dimensional data

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