Improved uncertainty capture for nonsingleton fuzzy systems

Pourabdollah, A. ORCID: 0000-0001-7737-1393, Wagner, C., Aladi, J.H. and Garibaldi, J.M., 2016. Improved uncertainty capture for nonsingleton fuzzy systems. IEEE Transactions on Fuzzy Systems, 24 (6), pp. 1513-1524. ISSN 1063-6706

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Abstract

In non-singleton fuzzy logic systems (NSFLSs), input uncertainties are modelled with input fuzzy sets in order to capture input uncertainty (e.g., sensor noise). The performance of NSFLSs in handling such uncertainties depends on both: the appropriate modelling in the input fuzzy sets of the uncertainties present in the system’s inputs, and on how the input fuzzy sets (and their inherent model of uncertainty) interact with the antecedent and thus affect the inference within the remainder of the NSFLS. This paper proposes a novel development on the latter. Specifically, an alteration to the standard composition method of type-1 fuzzy relations is proposed, and applied to build a new type of NSFLS. The proposed approach is based on employing the centroid of the intersection of input and antecedent sets as origin of the firing degree, rather than the traditional maximum of their intersection, thus making the NSFLS more sensitive to changes in the input’s uncertainty characteristics. The traditional and novel approach to NSFLSs are experimentally compared for two well-known problems of Mackey-Glass and Lorenz chaotic time series predictions, where the NSFLSs’ inputs have been perturbed with different levels of Gaussian noise. Experiments are repeated for system training under noisy and noise-free conditions. Analyses of the results show that the new method outperforms the traditional approach. Moreover, it is shown that while formally more complex, in practice, the new method has no significant computational overhead compared to the standard approach.

Item Type: Journal article
Publication Title: IEEE Transactions on Fuzzy Systems
Creators: Pourabdollah, A., Wagner, C., Aladi, J.H. and Garibaldi, J.M.
Publisher: IEEE Computational Intelligence Society
Date: December 2016
Volume: 24
Number: 6
ISSN: 1063-6706
Identifiers:
NumberType
10.1109/TFUZZ.2016.2540065DOI
Divisions: Schools > School of Science and Technology
Depositing User: Jill Tomkinson
Date Added: 23 Mar 2018 17:37
Last Modified: 23 Mar 2018 17:37
URI: http://irep.ntu.ac.uk/id/eprint/33087

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