  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.
