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

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

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: https://nottingham-repository.worktribe.com/output/878836
• Publisher URL: http://ieeexplore.ieee.org/abstract/document/8015575/
• Related Public URLs: http://dx.doi.org/10.1109/FUZZ-IEEE.2017.8015575
• 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.