# A Deep Neuro-Fuzzy Network for Image Classification

arXiv: https://arxiv.org/abs/2001.01686 · HTML: https://mlsanity.com/paper/2001.01686/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System](https://mlsanity.com/paper/2308.06378/) (2023)
- [Image Classification using Fuzzy Pooling in Convolutional Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2407.16268/) (2024)
- [Selected aspects of complex, hypercomplex and fuzzy neural networks](https://mlsanity.com/paper/2301.00007/) (2022)
- [HQFNN: A Compact Quantum-Fuzzy Neural Network for Accurate Image Classification](https://mlsanity.com/paper/2506.11146/) (2025)

## Abstract (arXiv)

The combination of neural network and fuzzy systems into neuro-fuzzy systems integrates fuzzy reasoning rules into the connectionist networks. However, the existing neuro-fuzzy systems are developed under shallow structures having lower generalization capacity. We propose the first end-to-end deep neuro-fuzzy network and investigate its application for image classification. Two new operations are developed based on definitions of Takagi-Sugeno-Kang (TSK) fuzzy model namely fuzzy inference operation and fuzzy pooling operations; stacks of these operations comprise the layers in this network. We evaluate the network on MNIST, CIFAR-10 and CIFAR-100 datasets, finding that the network has a reasonable accuracy in these benchmarks.