Determining firing strengths through a novel similarity measure to enhance uncertainty handling in non-singleton fuzzy logic systems

Pekaslan, Direnc and Garibaldi, Jonathan M. and Wagner, Christian (2017) Determining firing strengths through a novel similarity measure to enhance uncertainty handling in non-singleton fuzzy logic systems. In: International Joint Conference on Computational Intelligence (IJCCI 2017), 1-3 November 2017, Madeira, Portugal.

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Abstract

Non-Singleton Fuzzy Logic Systems (NSFLSs) have the potential to tackle uncertainty within the design of fuzzy systems. The inference process has a major role in determining results, being partly based on the interaction of input and antecedent fuzzy sets (in generating firing levels). Recent studies have shown that the standard technique for determining firing strengths risks substantial information loss in terms of the interaction of the input and antecedents. To address this issue, alternative approaches, which employ the centroid of intersections (cen-NS) and similarity measures (sim-NS), have been developed. More recently, a novel similarity measure for fuzzy sets has been introduced, but as yet this has not been used for NSFLSs. This paper focuses on exploring the potential of this new similarity measure in combination with the sim-NS approach to generate a more suitable firing level for non-singleton input. Experiments are presented for fuzzy systems trained using both noisy and noise-free time series. The prediction results of NSFLSs for the novel similarity measure and the current approaches are compared. Analysis of the results shows that the novel similarity measure, used within the sim-NS approach, can be a more stable and suitable method suitable to be used in real world applications.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/892619
Keywords: Inference Based, Firing Strength, Similarity Measure, Non-singleton, Noise/Uncertainty, Time Series Prediction
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.5220/0006502000830090
Depositing User: Pekaslan, Direnc
Date Deposited: 31 Jul 2018 10:30
Last Modified: 04 May 2020 19:16
URI: http://eprints.nottingham.ac.uk/id/eprint/53208

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