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

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

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.

Citation

Wagner, C., Pourabdollah, A., McCulloch, J., John, R., & Garibaldi, J. M. (2016, July). A similarity-based inference engine for non-singleton fuzzy logic systems. Presented at 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), Vancouver, BC, Canada

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016)
• Start Date: Jul 24, 2016
• End Date: Jul 29, 2016
• Acceptance Date: Apr 8, 2016
• Online Publication Date: Nov 10, 2016
• Publication Date: 2016
• Deposit Date: May 9, 2016
• Publicly Available Date: Nov 10, 2016
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 316-323
• Book Title: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
• ISBN: 978-1-5090-0627-4
• DOI: https://doi.org/10.1109/FUZZ-IEEE.2016.7737703
• Keywords: non-singleton, fuzzy logic systems, uncertainty, fuzzifier, input, similarity, time series prediction
• Public URL: https://nottingham-repository.worktribe.com/output/798412
• Publisher URL: https://ieeexplore.ieee.org/document/7737703/
• 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.
• Contract Date: May 9, 2016