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A support vector-based interval type-2 fuzzy system

Uslan, V.; Seker, H.; John, Robert

Authors

V. Uslan

H. Seker

Robert John robert.john@nottingham.ac.uk



Abstract

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.

Publication Date Jul 1, 2014
Peer Reviewed Peer Reviewed
APA6 Citation Uslan, V., Seker, H., & John, R. (2014). A support vector-based interval type-2 fuzzy system
Keywords 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;sup
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6891813
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information Published in: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. ISBN: 978-1-4799-2073-0, pp. 2396-2401, doi: 10.1109/FUZZ-IEEE.2014.6891813

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





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