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Evolving takagi sugeno modelling with memory for slow processes

Evolving takagi sugeno modelling with memory for slow processes 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Knowledge-Based and Intelligent Engineering Systems IOS Press

Evolving takagi sugeno modelling with memory for slow processes

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Publisher
IOS Press
Copyright
Copyright © 2010 by IOS Press, Inc
ISSN
1327-2314
eISSN
1875-8827
DOI
10.3233/KES-2010-0186
Publisher site
See Article on Publisher Site

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.

Journal

International Journal of Knowledge-Based and Intelligent Engineering Systems – IOS Press

Published: Jan 1, 2010

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