Skip to main content

Research Repository

Advanced Search

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

Wagner, Christian; Pourabdollah, Amir; McCulloch, Josie; John, Robert; Garibaldi, Jonathan M.

Authors

Christian Wagner christian.wagner@nottingham.ac.uk

Amir Pourabdollah amir.pourabdollah@nottingham.ac.uk

Josie McCulloch psxjm5@nottingham.ac.uk

Robert John robert.john@nottingham.ac.uk

Jonathan M. Garibaldi jon.garibaldi@nottingham.ac.uk



Abstract

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.

Start Date Jul 24, 2016
Publication Date Jul 24, 2016
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
APA6 Citation Wagner, C., Pourabdollah, A., McCulloch, J., John, R., & Garibaldi, J. M. (2016). A similarity-based inference engine for non-singleton fuzzy logic systems. doi:10.1109/FUZZ-IEEE.2016.7737703
DOI https://doi.org/10.1109/FUZZ-IEEE.2016.7737703
Keywords non-singleton, fuzzy logic systems, uncertainty, fuzzifier, input, similarity, time series prediction
Publisher URL https://ieeexplore.ieee.org/document/7737703/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
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.

Files

paper-ver03.pdf (1.7 Mb)
PDF

Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





You might also like



Downloadable Citations

;