Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels

Pekaslan, Direnc; Wagner, Christian; Garibaldi, Jonathan M.

Abstract

Most real-world environments are subject to different sources of uncertainty which may vary in magnitude over time. We propose that while Type-1 (T1) Non-Singleton Fuzzy Logic System (NSFLSs) have the potential to tackle uncertainty within the input Fuzzy Sets (FSs), Type-2 (T2) input FSs provide the ability to also capture variation in uncertainty levels by means of their extra degrees of freedom. Specifically, in this paper, we propose a strategy to design Interval Type-2 (IT2) input Membership Functions (MFs) in an online manner to ensure the parameters of input MFs are updated dynamically, thus capturing varying levels of uncertainty affecting systems' inputs. In this strategy, first, uncertainty detection is performed over a given time-frame (the Uncertainty Estimation Time-frame) and Type-1 (T1) input MFs are constructed by utilising the detected uncertainty level. Second, the variation of the uncertainty levels over a sliding window (the Uncertainty Variation Window) is used to capture the degree of variation in the detected uncertainty levels over time, which in turn informs the size of the Footprint of Uncertainty (FOU) of the IT2 MF associated with the T1 principal MF. Using time-series prediction experiments as an initial evaluation and demonstration platform for the proposed architecture, we show that the proposed strategy of designing IT2 input MFs has the potential to deliver performance benefits. Specifically, it allows systems to not only adapt to specific uncertainty levels but also to be more resilient to the variation of said uncertainty levels over time, thus offering a pathway to robust performance in real-world applications.

Citation

Pekaslan, D., Wagner, C., & Garibaldi, J. M. (2019, June). Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
• Start Date: Jun 23, 2019
• End Date: Jun 26, 2019
• Acceptance Date: Mar 7, 2019
• Online Publication Date: Oct 11, 2019
• Publication Date: 2019-06
• Deposit Date: Nov 12, 2019
• Publicly Available Date: Nov 12, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1-7
• Series ISSN: 1558-4739
• Book Title: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
• ISBN: 978-1-5386-1729-8
• DOI: https://doi.org/10.1109/FUZZ-IEEE.2019.8858800
• Public URL: https://nottingham-repository.worktribe.com/output/3231588
• Publisher URL: https://ieeexplore.ieee.org/abstract/document/8858800
• Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
• Contract Date: Nov 12, 2019