# A Temporal Type-2 Fuzzy System for Time-dependent Explainable Artificial Intelligence

arXiv: https://arxiv.org/abs/2210.12571 · HTML: https://mlsanity.com/paper/2210.12571/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 22 Oct 2022.

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

- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels](https://mlsanity.com/paper/2111.05895/) (2021)
- [Brain Intelligence: Go Beyond Artificial Intelligence](https://mlsanity.com/paper/1706.01040/) (2017)
- [A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation](https://mlsanity.com/paper/2004.07995/) (2020)
- [Towards Understanding Human Functional Brain Development with Explainable Artificial Intelligence: Challenges and Perspectives](https://mlsanity.com/paper/2112.12910/) (2021)

## Built on by (most cited first, 7 of 7)

- [ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques](https://mlsanity.com/paper/2308.15284/) (2023)
- [EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition](https://mlsanity.com/paper/2310.03754/) (2023)
- [Reliable Classification with Conformal Learning and Interval-Type 2 Fuzzy Sets](https://mlsanity.com/paper/2504.15360/) (2025)
- [An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models](https://mlsanity.com/paper/2511.08077/) (2025)
- [Interpreting Contrastive Embeddings in Specific Domains with Fuzzy Rules](https://mlsanity.com/paper/2603.12227/) (2026)
- [Interpretable Fuzzy Rule-Based Regression Extension for Ex-Fuzzy Library](https://mlsanity.com/paper/2607.20277/) (2026)
- [A Framework for Reputation Aware Uninorm-driven Consensus Algorithms for Blockchain Networks](https://mlsanity.com/paper/2607.20700/) (2026)

## Abstract (arXiv)

Explainable Artificial Intelligence (XAI) is a paradigm that delivers transparent models and decisions, which are easy to understand, analyze, and augment by a non-technical audience. Fuzzy Logic Systems (FLS) based XAI can provide an explainable framework, while also modeling uncertainties present in real-world environments, which renders it suitable for applications where explainability is a requirement. However, most real-life processes are not characterized by high levels of uncertainties alone; they are inherently time-dependent as well, i.e., the processes change with time. In this work, we present novel Temporal Type-2 FLS Based Approach for time-dependent XAI (TXAI) systems, which can account for the likelihood of a measurement's occurrence in the time domain using (the measurement's) frequency of occurrence. In Temporal Type-2 Fuzzy Sets (TT2FSs), a four-dimensional (4D) time-dependent membership function is developed where relations are used to construct the inter-relations between the elements of the universe of discourse and its frequency of occurrence. The TXAI system manifested better classification prowess, with 10-fold test datasets, with a mean recall of 95.40% than a standard XAI system (based on non-temporal general type-2 (GT2) fuzzy sets) that had a mean recall of 87.04%. TXAI also performed significantly better than most non-explainable AI systems between 3.95%, to 19.04% improvement gain in mean recall. In addition, TXAI can also outline the most likely time-dependent trajectories using the frequency of occurrence values embedded in the TXAI model; viz. given a rule at a determined time interval, what will be the next most likely rule at a subsequent time interval. In this regard, the proposed TXAI system can have profound implications for delineating the evolution of real-life time-dependent processes, such as behavioural or biological processes.