A support vector-based interval type-2 fuzzy system
In this paper, a new fuzzy regression model that is supported by support vector regression is presented. Type-2 fuzzy systems are able to tackle applications that have significant uncertainty. However general type-2 fuzzy systems are more complex than type-1 fuzzy systems. Support vector machines ar...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2014
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| Online Access: | https://eprints.nottingham.ac.uk/27775/ |
| _version_ | 1848793435638595584 |
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| author | Uslan, V. Seker, H. John, Robert |
| author_facet | Uslan, V. Seker, H. John, Robert |
| author_sort | Uslan, V. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In this paper, a new fuzzy regression model that is supported by support vector regression is presented. Type-2 fuzzy systems are able to tackle applications that have significant uncertainty. However general type-2 fuzzy systems are more complex than type-1 fuzzy systems. Support vector machines are similar to fuzzy systems in that they can also model systems that are non-linear in nature. In the proposed model the consequent parameters of type-2 fuzzy rules are learnt using support vector regression and an efficient closed-form type reduction strategy is used to simplify the computations. Support vector regression improved the generalisation performance of the fuzzy rule-based system in which the fuzzy rules were a set of interpretable IF-THEN rules. The performance of the proposed model was demonstrated by conducting case studies for the non-linear system approximation and prediction of chaotic time series. The model yielded promising results and the simulation results are compared to the results published in the area. |
| first_indexed | 2025-11-14T19:00:15Z |
| format | Conference or Workshop Item |
| id | nottingham-27775 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:00:15Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-277752020-05-04T20:14:14Z https://eprints.nottingham.ac.uk/27775/ A support vector-based interval type-2 fuzzy system Uslan, V. Seker, H. John, Robert In this paper, a new fuzzy regression model that is supported by support vector regression is presented. Type-2 fuzzy systems are able to tackle applications that have significant uncertainty. However general type-2 fuzzy systems are more complex than type-1 fuzzy systems. Support vector machines are similar to fuzzy systems in that they can also model systems that are non-linear in nature. In the proposed model the consequent parameters of type-2 fuzzy rules are learnt using support vector regression and an efficient closed-form type reduction strategy is used to simplify the computations. Support vector regression improved the generalisation performance of the fuzzy rule-based system in which the fuzzy rules were a set of interpretable IF-THEN rules. The performance of the proposed model was demonstrated by conducting case studies for the non-linear system approximation and prediction of chaotic time series. The model yielded promising results and the simulation results are compared to the results published in the area. 2014-07 Conference or Workshop Item PeerReviewed Uslan, V., Seker, H. and John, Robert (2014) A support vector-based interval type-2 fuzzy system. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 6-11 July, Beijing, China. fuzzy set theory;regression analysis;support vector machines;IF-THEN rules;efficient closed-form type reduction strategy;fuzzy regression model;generalisation performance;nonlinear system approximation;support vector machines;support vector regression;support vector-based interval type-2 fuzzy system;Approximation methods;Computational modeling;Fuzzy sets;Fuzzy systems;Predictive models;Support vector machines;Time series analysis http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6891813 |
| spellingShingle | fuzzy set theory;regression analysis;support vector machines;IF-THEN rules;efficient closed-form type reduction strategy;fuzzy regression model;generalisation performance;nonlinear system approximation;support vector machines;support vector regression;support vector-based interval type-2 fuzzy system;Approximation methods;Computational modeling;Fuzzy sets;Fuzzy systems;Predictive models;Support vector machines;Time series analysis Uslan, V. Seker, H. John, Robert A support vector-based interval type-2 fuzzy system |
| title | A support vector-based interval type-2 fuzzy system |
| title_full | A support vector-based interval type-2 fuzzy system |
| title_fullStr | A support vector-based interval type-2 fuzzy system |
| title_full_unstemmed | A support vector-based interval type-2 fuzzy system |
| title_short | A support vector-based interval type-2 fuzzy system |
| title_sort | support vector-based interval type-2 fuzzy system |
| topic | fuzzy set theory;regression analysis;support vector machines;IF-THEN rules;efficient closed-form type reduction strategy;fuzzy regression model;generalisation performance;nonlinear system approximation;support vector machines;support vector regression;support vector-based interval type-2 fuzzy system;Approximation methods;Computational modeling;Fuzzy sets;Fuzzy systems;Predictive models;Support vector machines;Time series analysis |
| url | https://eprints.nottingham.ac.uk/27775/ https://eprints.nottingham.ac.uk/27775/ |