A similarity-based inference engine for non-singleton fuzzy logic systems

Wagner, Christian and Pourabdollah, Amir and McCulloch, Josie and John, Robert and Garibaldi, Jonathan M. (2016) A similarity-based inference engine for non-singleton fuzzy logic systems. In: IEEE International Conference on Fuzzy Systems 2016 (FUZZ-IEEE 2016), 24-29th July 2016, Vancouver, Canada. (Unpublished)

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In non-singleton fuzzy logic systems (NSFLSs) input uncertainties are modelled with input fuzzy sets in order to capture input uncertainty such as sensor noise. The performance of NSFLSs in handling such uncertainties depends both on the actual input fuzzy sets (and their inherent model of uncertainty) and on the way that they affect the inference process. This paper proposes a novel type of NSFLS by replacing the composition-based inference method of type-1 fuzzy relations with a similarity-based inference method that makes NSFLSs more sensitive to changes in the input's uncertainty characteristics. The proposed approach is based on using the Jaccard ratio to measure the similarity between input and antecedent fuzzy sets, then using the measured similarity to determine the firing strength of each individual fuzzy rule. The standard and novel approaches to NSFLSs are experimentally compared for the well-known problem of Mackey-Glass time series predictions, where the NSFLS's inputs have been perturbed with different levels of Gaussian noise. The experiments are repeated for system training under both noisy and noise-free conditions. Analyses of the results show that the new method outperforms the standard approach by substantially reducing the prediction errors.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2016 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.
Keywords: non-singleton, fuzzy logic systems, uncertainty, fuzzifier, input, similarity, time series prediction
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Depositing User: Wagner, Dr Christian
Date Deposited: 09 May 2016 10:22
Last Modified: 18 Sep 2016 07:17
URI: http://eprints.nottingham.ac.uk/id/eprint/33186

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