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A new dynamic approach for non-singleton fuzzification in noisy time-series prediction

Pourabdollah, Amir; John, Robert; Garibaldi, Jonathan M.

Authors

Amir Pourabdollah amir.pourabdollah@nottingham.ac.uk

Robert John robert.john@nottingham.ac.uk



Abstract

Non-singleton fuzzification is used to model uncertain (e.g. noisy) inputs within fuzzy logic systems. In the standard approach, assuming the fuzzification type is known, the observed [noisy] input is usually considered to be the core of the input fuzzy set, usually being the centre of its membership function. This paper proposes a new fuzzification method (not type), in which the core of an input fuzzy set is not necessarily located at the observed input, rather it is dynamically adjusted based on statistical methods. Using the weighted moving average, a few past samples are aggregated to roughly estimate where the input fuzzy set should be located. While the added complexity is not huge, applying this method to the well-known Mackey-Glass and Lorenz time-series prediction problems, show significant error reduction when the input is corrupted by different noise levels.

Citation

Pourabdollah, A., John, R., & Garibaldi, J. M. (in press). A new dynamic approach for non-singleton fuzzification in noisy time-series prediction

Conference Name 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
End Date Jul 12, 2017
Acceptance Date Mar 14, 2017
Online Publication Date Aug 24, 2017
Deposit Date Aug 30, 2017
Publicly Available Date Aug 30, 2017
Electronic ISSN 1558-4739
Peer Reviewed Peer Reviewed
Keywords Noise measurement, Standards, Fuzzy sets, Fuzzy logic, Uncertainty, Time series analysis, Estimation
Public URL http://eprints.nottingham.ac.uk/id/eprint/45209
Publisher URL http://ieeexplore.ieee.org/abstract/document/8015575/
Related Public URLs http://dx.doi.org/10.1109/FUZZ-IEEE.2017.8015575
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
Additional Information ISSN 1558-4739. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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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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