# A Deep Neuro-Fuzzy Network for Image Classification

Source: [A Deep Neuro-Fuzzy Network for Image Classification](<https://www.sota2.com/research/paper/68fa66e91e209131cea131cd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:01:21.007Z

Authors: Omolbanin Yazdanbakhsh, Scott Dick

Published: 2019-12-22T03:28:05.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2001.01686>)

[PDF](<https://arxiv.org/pdf/2001.01686.pdf>)