Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems

Aladi, J.H., Wagner, C., Pourabdollah, A. ORCID: 0000-0001-7737-1393 and Garibaldi, J.M., 2016. Contrasting singleton type-1 and interval type-2 non-singleton type-1 fuzzy logic systems. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, Canada, 24-29 July 2016. Piscataway, NJ: Institute of Electrical and Electronics Engineers, pp. 2043-2050. ISBN 9781509006267

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Abstract

Most applications of both type-1 and type-2 fuzzy logic systems are employing singleton fuzzification due to its simplicity and reduction in its computational speed. However, using singleton fuzzification assumes that the input data (i.e., measurements) are precise with no uncertainty associated with them. This paper explores the potential of combining the uncertainty modelling capacity of interval type-2 fuzzy sets with the simplicity of type-1 fuzzy logic systems (FLSs) by using interval type-2 fuzzy sets solely as part of the non-singleton input fuzzifier. This paper builds on previous work and uses the methodological design of the footprint of uncertainty (FOU) of interval type-2 fuzzy sets for given levels of uncertainty. We provide a detailed investigation into the ability of both types of fuzzy sets (type-1 and interval type-2) to capture and model different levels of uncertainty/noise through varying the size of the FOU of the underlying input fuzzy sets from type-1 fuzzy sets to very “wide” interval type-2 fuzzy sets as part of type-1 non-singleton FLSs using interval type-2 input fuzzy sets. By applying the study in the context of chaotic time-series prediction, we show how, as uncertainty/noise increases, interval type-2 input fuzzy sets with FOUs of increasing size become more and more viable.

Item Type: Chapter in book
Creators: Aladi, J.H., Wagner, C., Pourabdollah, A. and Garibaldi, J.M.
Publisher: Institute of Electrical and Electronics Engineers
Place of Publication: Piscataway, NJ
Date: 2016
Identifiers:
NumberType
10.1109/FUZZ-IEEE.2016.7737943DOI
Divisions: Schools > School of Science and Technology
Depositing User: Jonathan Gallacher
Date Added: 06 Apr 2018 08:06
Last Modified: 06 Apr 2018 08:06
URI: http://irep.ntu.ac.uk/id/eprint/33209

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