PyTSK: A Python Toolbox for TSK Fuzzy Systems
Not on the map. Why?
Abstract (arXiv)
This paper presents PyTSK, a Python toolbox for developing Takagi-Sugeno-Kang (TSK) fuzzy systems. Based on scikit-learn and PyTorch, PyTSK allows users to optimize TSK fuzzy systems using fuzzy clustering or mini-batch gradient descent (MBGD) based algorithms. Several state-of-the-art MBGD-based optimization algorithms are implemented in the toolbox, which can improve the generalization performance of TSK fuzzy systems, especially for big data applications. PyTSK can also be easily extended and customized for more complicated algorithms, such as modifying the structure of TSK fuzzy systems, developing more sophisticated training algorithms, and combining TSK fuzzy systems with neural networks. The code of PyTSK can be found at https://github.com/YuqiCui/pytsk.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 15 references that are in the map. First posted on arXiv 7 Jun 2022.
Builds on
5 of 15 references.
References · 5
- 2021Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutionscited by 12
- 2019Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA)cited by 9
- 2019Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learningcited by 3
- 2020FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBeliefcited by 3
- 2019Patch Learningcited by 2
Built on by
Citing papers per year: 2026: 2.
2 papers.