A new dynamic approach for non-singleton fuzzification in noisy time-series prediction
Non-singleton fuzzification is used to model uncertain (e.g. noisy) inputs within fuzzy logic systems. In the standard approach, assuming the fuzzification type is known, the observed [noisy] input is usually considered to be the core of the input fuzzy set, usually being the centre of its membershi...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2017
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| Online Access: | https://eprints.nottingham.ac.uk/45209/ |
| _version_ | 1848797090095824896 |
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| author | Pourabdollah, Amir John, Robert Garibaldi, Jonathan M. |
| author_facet | Pourabdollah, Amir John, Robert Garibaldi, Jonathan M. |
| author_sort | Pourabdollah, Amir |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Non-singleton fuzzification is used to model uncertain (e.g. noisy) inputs within fuzzy logic systems. In the standard approach, assuming the fuzzification type is known, the observed [noisy] input is usually considered to be the core of the input fuzzy set, usually being the centre of its membership function. This paper proposes a new fuzzification method (not type), in which the core of an input fuzzy set is not necessarily located at the observed input, rather it is dynamically adjusted based on statistical methods. Using the weighted moving average, a few past samples are aggregated to roughly estimate where the input fuzzy set should be located. While the added complexity is not huge, applying this method to the well-known Mackey-Glass and Lorenz time-series prediction problems, show significant error reduction when the input is corrupted by different noise levels. |
| first_indexed | 2025-11-14T19:58:21Z |
| format | Conference or Workshop Item |
| id | nottingham-45209 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:58:21Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-452092020-05-04T19:01:59Z https://eprints.nottingham.ac.uk/45209/ A new dynamic approach for non-singleton fuzzification in noisy time-series prediction Pourabdollah, Amir John, Robert Garibaldi, Jonathan M. Non-singleton fuzzification is used to model uncertain (e.g. noisy) inputs within fuzzy logic systems. In the standard approach, assuming the fuzzification type is known, the observed [noisy] input is usually considered to be the core of the input fuzzy set, usually being the centre of its membership function. This paper proposes a new fuzzification method (not type), in which the core of an input fuzzy set is not necessarily located at the observed input, rather it is dynamically adjusted based on statistical methods. Using the weighted moving average, a few past samples are aggregated to roughly estimate where the input fuzzy set should be located. While the added complexity is not huge, applying this method to the well-known Mackey-Glass and Lorenz time-series prediction problems, show significant error reduction when the input is corrupted by different noise levels. 2017-08-24 Conference or Workshop Item PeerReviewed Pourabdollah, Amir, John, Robert and Garibaldi, Jonathan M. (2017) A new dynamic approach for non-singleton fuzzification in noisy time-series prediction. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 9-12 July 2017, Naples, Italy. Noise measurement Standards Fuzzy sets Fuzzy logic Uncertainty Time series analysis Estimation http://ieeexplore.ieee.org/abstract/document/8015575/ |
| spellingShingle | Noise measurement Standards Fuzzy sets Fuzzy logic Uncertainty Time series analysis Estimation Pourabdollah, Amir John, Robert Garibaldi, Jonathan M. A new dynamic approach for non-singleton fuzzification in noisy time-series prediction |
| title | A new dynamic approach for non-singleton fuzzification in noisy time-series prediction |
| title_full | A new dynamic approach for non-singleton fuzzification in noisy time-series prediction |
| title_fullStr | A new dynamic approach for non-singleton fuzzification in noisy time-series prediction |
| title_full_unstemmed | A new dynamic approach for non-singleton fuzzification in noisy time-series prediction |
| title_short | A new dynamic approach for non-singleton fuzzification in noisy time-series prediction |
| title_sort | new dynamic approach for non-singleton fuzzification in noisy time-series prediction |
| topic | Noise measurement Standards Fuzzy sets Fuzzy logic Uncertainty Time series analysis Estimation |
| url | https://eprints.nottingham.ac.uk/45209/ https://eprints.nottingham.ac.uk/45209/ |