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Evolving Takagi Sugeno modelling with memory for slow processes.

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Evolving Takagi Sugeno modelling with memory for slow processes. / McDonald, Simon; Angelov, Plamen.

In: International Journal of Knowledge-Based and Intelligent Engineering Systems, Vol. 14, No. 1, 02.2010, p. 11-16.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

McDonald, S & Angelov, P 2010, 'Evolving Takagi Sugeno modelling with memory for slow processes.', International Journal of Knowledge-Based and Intelligent Engineering Systems, vol. 14, no. 1, pp. 11-16. <http://www.kesinternational.org/journal/>

APA

McDonald, S., & Angelov, P. (2010). Evolving Takagi Sugeno modelling with memory for slow processes. International Journal of Knowledge-Based and Intelligent Engineering Systems, 14(1), 11-16. http://www.kesinternational.org/journal/

Vancouver

McDonald S, Angelov P. Evolving Takagi Sugeno modelling with memory for slow processes. International Journal of Knowledge-Based and Intelligent Engineering Systems. 2010 Feb;14(1):11-16.

Author

McDonald, Simon ; Angelov, Plamen. / Evolving Takagi Sugeno modelling with memory for slow processes. In: International Journal of Knowledge-Based and Intelligent Engineering Systems. 2010 ; Vol. 14, No. 1. pp. 11-16.

Bibtex

@article{c73b8cc19e9947c097b3086a5579a5fb,
title = "Evolving Takagi Sugeno modelling with memory for slow processes.",
abstract = "Evolving Takagi Sugeno (eTS) models are optimised for use in applications with high sampling rates. This mode of use produces excellent prediction results very quickly and with low memory requirements, even with large numbers of input attributes. In this paper eTS modelling is adapted for optimality in situations where memory usage and processing time are not specific requirements. The new method, eTS with memory, is demonstrated on two financial time series, both the fullband signals and after decomposition by the discrete wavelet transform. It is shown that the use of previous inputs and multiple iterations in eTS can produce better predictions for signals which are not dominated by the characteristics of noise.",
keywords = "Evolving Takagi Sugeno, Fuzzy, Modelling, Noise, Discrete Wavelet Transform",
author = "Simon McDonald and Plamen Angelov",
year = "2010",
month = feb,
language = "English",
volume = "14",
pages = "11--16",
journal = "International Journal of Knowledge-Based and Intelligent Engineering Systems",
issn = "1327-2314",
publisher = "IOS Press",
number = "1",

}

RIS

TY - JOUR

T1 - Evolving Takagi Sugeno modelling with memory for slow processes.

AU - McDonald, Simon

AU - Angelov, Plamen

PY - 2010/2

Y1 - 2010/2

N2 - Evolving Takagi Sugeno (eTS) models are optimised for use in applications with high sampling rates. This mode of use produces excellent prediction results very quickly and with low memory requirements, even with large numbers of input attributes. In this paper eTS modelling is adapted for optimality in situations where memory usage and processing time are not specific requirements. The new method, eTS with memory, is demonstrated on two financial time series, both the fullband signals and after decomposition by the discrete wavelet transform. It is shown that the use of previous inputs and multiple iterations in eTS can produce better predictions for signals which are not dominated by the characteristics of noise.

AB - Evolving Takagi Sugeno (eTS) models are optimised for use in applications with high sampling rates. This mode of use produces excellent prediction results very quickly and with low memory requirements, even with large numbers of input attributes. In this paper eTS modelling is adapted for optimality in situations where memory usage and processing time are not specific requirements. The new method, eTS with memory, is demonstrated on two financial time series, both the fullband signals and after decomposition by the discrete wavelet transform. It is shown that the use of previous inputs and multiple iterations in eTS can produce better predictions for signals which are not dominated by the characteristics of noise.

KW - Evolving Takagi Sugeno

KW - Fuzzy

KW - Modelling

KW - Noise

KW - Discrete Wavelet Transform

M3 - Journal article

VL - 14

SP - 11

EP - 16

JO - International Journal of Knowledge-Based and Intelligent Engineering Systems

JF - International Journal of Knowledge-Based and Intelligent Engineering Systems

SN - 1327-2314

IS - 1

ER -