# Efficient Learning of Fuzzy Logic Systems for Large-Scale Data Using Deep Learning

arXiv: https://arxiv.org/abs/2404.12792 · HTML: https://mlsanity.com/paper/2404.12792/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques](https://mlsanity.com/paper/2308.15284/) (2023)
- [Introducing Fuzzy Layers for Deep Learning](https://mlsanity.com/paper/2003.00880/) (2020)

## Built on by (most cited first, 1 of 1)

- [FAME: Introducing Fuzzy Additive Models for Explainable AI](https://mlsanity.com/paper/2504.07011/) (2025)

## Abstract (arXiv)

Type-1 and Interval Type-2 (IT2) Fuzzy Logic Systems (FLS) excel in handling uncertainty alongside their parsimonious rule-based structure. Yet, in learning large-scale data challenges arise, such as the curse of dimensionality and training complexity of FLSs. The complexity is due mainly to the constraints to be satisfied as the learnable parameters define FSs and the complexity of the center of the sets calculation method, especially of IT2-FLSs. This paper explicitly focuses on the learning problem of FLSs and presents a computationally efficient learning method embedded within the realm of Deep Learning (DL). The proposed method tackles the learning challenges of FLSs by presenting computationally efficient implementations of FLSs, thereby minimizing training time while leveraging mini-batched DL optimizers and automatic differentiation provided within the DL frameworks. We illustrate the efficiency of the DL framework for FLSs on benchmark datasets.