A similarity-based inference engine for non-singleton fuzzy logic systems
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 uncertain...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2016
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| Online Access: | https://eprints.nottingham.ac.uk/33186/ |
| _version_ | 1848794578124013568 |
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| author | Wagner, Christian Pourabdollah, Amir McCulloch, Josie John, Robert Garibaldi, Jonathan M. |
| author_facet | Wagner, Christian Pourabdollah, Amir McCulloch, Josie John, Robert Garibaldi, Jonathan M. |
| author_sort | Wagner, Christian |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:18:25Z |
| format | Conference or Workshop Item |
| id | nottingham-33186 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:18:25Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-331862020-05-04T17:58:58Z https://eprints.nottingham.ac.uk/33186/ A similarity-based inference engine for non-singleton fuzzy logic systems Wagner, Christian Pourabdollah, Amir McCulloch, Josie John, Robert Garibaldi, Jonathan M. 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. 2016-07-30 Conference or Workshop Item PeerReviewed Wagner, Christian, Pourabdollah, Amir, McCulloch, Josie, 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) non-singleton fuzzy logic systems uncertainty fuzzifier input similarity time series prediction |
| spellingShingle | non-singleton fuzzy logic systems uncertainty fuzzifier input similarity time series prediction Wagner, Christian Pourabdollah, Amir McCulloch, Josie John, Robert Garibaldi, Jonathan M. A similarity-based inference engine for non-singleton fuzzy logic systems |
| title | A similarity-based inference engine for non-singleton fuzzy logic systems |
| title_full | A similarity-based inference engine for non-singleton fuzzy logic systems |
| title_fullStr | A similarity-based inference engine for non-singleton fuzzy logic systems |
| title_full_unstemmed | A similarity-based inference engine for non-singleton fuzzy logic systems |
| title_short | A similarity-based inference engine for non-singleton fuzzy logic systems |
| title_sort | similarity-based inference engine for non-singleton fuzzy logic systems |
| topic | non-singleton fuzzy logic systems uncertainty fuzzifier input similarity time series prediction |
| url | https://eprints.nottingham.ac.uk/33186/ |