Noise parameter estimation for non-singleton fuzzy logic systems

Pekaslan, Direnc and Garibaldi, Jonathan M. and Wagner, Christian (2018) Noise parameter estimation for non-singleton fuzzy logic systems. In: IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2018), 7-10 October 2018, Miyazaki, Japan. (Submitted)

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Abstract

Real-world environments face a wide range of noise (uncertainty) sources and gaining insight into the level of noise is a critical part of many applications. While Non-Singleton Fuzzy Logic Systems (NSFLSs), in particular recently introduced advanced variants such as centroid-based NSFLSs have the capacity to handle known quantities of uncertainty, thus far, the actual level of uncertainty has had to be defined a priori - i.e. prior to run time of a system or controller. This paper does not focus on such advances within the architecture of NSFLSs, but focuses on a novel two-stage approach for uncertainty handling in fuzzy logic systems which integrates: (i) estimation of noise levels and (ii) the appropriate handling of the noise based on this estimate, by means of a dynamically configured NSFLS. As initial evaluation of the approach, two chaotic nonlinear time series (Mackey-Glass and Lorenz), as well as a real-world Darwin sea level pressure series prediction fuzzy logic systems are implemented and compared to commonly used procedures. The results indicate that the proposed strategy of integrating uncertainty/noise estimation with the capacity of non-singleton fuzzy logic systems has the potential to deliver performance benefits in real-world applications without requiring a priori information on noise levels and thus delivers a first step towards smart, noise-adaptive non-singleton fuzzy logic systems and controllers.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/939074
Additional Information: © 2018 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.
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Related URLs:
URLURL Type
http://www.smc2018.org/UNSPECIFIED
https://ieeexplore.ieee.org/UNSPECIFIED
Depositing User: Pekaslan, Direnc
Date Deposited: 27 Jul 2018 07:55
Last Modified: 04 May 2020 19:41
URI: http://eprints.nottingham.ac.uk/id/eprint/53167

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