# Recommendations on Designing Practical Interval Type-2 Fuzzy Systems

arXiv: https://arxiv.org/abs/1907.01697 · HTML: https://mlsanity.com/paper/1907.01697/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions](https://mlsanity.com/paper/2102.04271/) (2021)
- [Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks](https://mlsanity.com/paper/2210.07707/) (2022)
- [A Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks](https://mlsanity.com/paper/2207.09936/) (2022)
- [An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks](https://mlsanity.com/paper/2207.10282/) (2022)
- [Driving Style Recognition Using Interval Type-2 Fuzzy Inference System and Multiple Experts Decision Making](https://mlsanity.com/paper/2110.13805/) (2021)
- [Ontology-Based Knowledge Modeling and Uncertainty-Aware Outdoor Air Quality Assessment Using Weighted Interval Type-2 Fuzzy Logic](https://mlsanity.com/paper/2603.19683/) (2026)

## Abstract (arXiv)

Interval type-2 (IT2) fuzzy systems have become increasingly popular in the last 20 years. They have demonstrated superior performance in many applications. However, the operation of an IT2 fuzzy system is more complex than that of its type-1 counterpart. There are many questions to be answered in designing an IT2 fuzzy system: Should singleton or non-singleton fuzzifier be used? How many membership functions (MFs) should be used for each input? Should Gaussian or piecewise linear MFs be used? Should Mamdani or Takagi-Sugeno-Kang (TSK) inference be used? Should minimum or product t-norm be used? Should type-reduction be used or not? How to optimize the IT2 fuzzy system? These questions may look overwhelming and confusing to IT2 beginners. In this paper we recommend some representative starting choices for an IT2 fuzzy system design, which hopefully will make IT2 fuzzy systems more accessible to IT2 fuzzy system designers.