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

Pourabdollah, Amir and John, Robert and Garibaldi, Jonathan M. (2017) A new dynamic approach for non-singleton fuzzification in noisy time-series prediction. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 9-12 July 2017, Naples, Italy.

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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.

Item Type: Conference or Workshop Item (Paper)
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.
Keywords: Noise measurement, Standards, Fuzzy sets, Fuzzy logic, Uncertainty, Time series analysis, Estimation
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
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Depositing User: Pourabdollah, Amir
Date Deposited: 30 Aug 2017 08:25
Last Modified: 30 Aug 2017 08:28
URI: http://eprints.nottingham.ac.uk/id/eprint/45209

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