# PyTSK: A Python Toolbox for TSK Fuzzy Systems

arXiv: https://arxiv.org/abs/2206.03310 · HTML: https://mlsanity.com/paper/2206.03310/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 7 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions](https://mlsanity.com/paper/2102.04271/) (2021)
- [Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA)](https://mlsanity.com/paper/1903.10951/) (2019)
- [Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning](https://mlsanity.com/paper/1904.10683/) (2019)
- [FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief](https://mlsanity.com/paper/2012.00060/) (2020)
- [Patch Learning](https://mlsanity.com/paper/1906.00158/) (2019)

## Built on by (most cited first, 2 of 2)

- [Alternating Bi-Objective Optimization for Explainable Neuro-Fuzzy Systems](https://mlsanity.com/paper/2602.19253/) (2026)
- [Explainable machine learning workflows for radio astronomical data processing](https://mlsanity.com/paper/2603.16350/) (2026)

## 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.