The superior interpretability and uncertainty modeling ability of Takagi-Sugeno-Kang fuzzy system (TSK FS) make it possible to describe complex nonlinear systems intuitively and efficiently. However, classical TSK FS usually adopts the whole feature space of the data for model construction, which can result in lengthy rules for high-dimensional data and lead to degeneration in interpretability. Furthermore, for highly nonlinear modeling task, it is usually necessary to use a large number of rules which further weaken the clarity and interpretability of TSK FS. To address these issues, an enhanced soft subspace clustering (ESSC) and sparse learning (SL) based concise zero-order TSK FS construction method, called ESSC-SL-CTSK-FS, is proposed in this paper by integrating the techniques of ESSC and SL. In this method, ESSC is used to generate the antecedents and various sparse subspaces for different fuzzy rules, whereas SL is used to optimize the consequent parameters of the fuzzy rules based on which the number of fuzzy rules can be effectively reduced. Finally, the proposed ESSC-SL-CTSK-FS method is used to construct concise zero-order TSK FS that can explain the scenes in high-dimensional data modeling more clearly and easily. Experiments are conducted on various real-world datasets to confirm the advantages.

中文翻译：

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集成软子空间聚类和稀疏学习的简洁模糊系统建模


Takagi-Sugeno-Kang模糊系统（TSK FS）卓越的可解释性和不确定性建模能力使得直观有效地描述复杂非线性系统成为可能。然而，经典的TSK FS通常采用数据的整个特征空间来构建模型，这会导致高维数据的规则冗长并导致可解释性退化。此外，对于高度非线性的建模任务，通常需要使用大量的规则，这进一步削弱了TSK FS的清晰度和可解释性。为了解决这些问题，本文结合 ESSC 和稀疏学习的技术，提出了一种基于增强软子空间聚类（ESSC）和稀疏学习（SL）的简明零阶 TSK FS 构造方法，称为 ESSC-SL-CTSK-FS。 SL。该方法利用ESSC为不同的模糊规则生成前件和各种稀疏子​​空间，而SL则用来优化模糊规则的后件参数，在此基础上可以有效地减少模糊规则的数量。最后，使用所提出的ESSC-SL-CTSK-FS方法构建简洁的零阶TSK FS，可以更清晰、更容易地解释高维数据建模中的场景。在各种现实世界数据集上进行实验以确认其优势。