A support vector-based interval type-2 fuzzy system

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.

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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.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/995538
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
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;support vector-based interval type-2 fuzzy system;Approximation methods;Computational modeling;Fuzzy sets;Fuzzy systems;Predictive models;Support vector machines;Time series analysis
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: John, Professor Robert
Date Deposited: 02 Feb 2015 15:04
Last Modified: 04 May 2020 20:14
URI: https://eprints.nottingham.ac.uk/id/eprint/27775

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