Evolving Takagi Sugeno modelling with memory for slow processes.

McDonald, Simon and Angelov, Plamen (2010) Evolving Takagi Sugeno modelling with memory for slow processes. International Journal of Knowledge-Based and Intelligent Engineering Systems, 14 (1). pp. 11-16. ISSN 1327-2314

[img]
Preview
PDF (KES_Paper2009.pdf)
KES_Paper2009.pdf

Download (154kB)

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.

Item Type: Journal Article
Journal or Publication Title: International Journal of Knowledge-Based and Intelligent Engineering Systems
Uncontrolled Keywords: /dk/atira/pure/researchoutput/libraryofcongress/qa75
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 27111
Deposited By: Dr. Plamen Angelov
Deposited On: 30 Sep 2009 15:30
Refereed?: Yes
Published?: Published
Last Modified: 15 Sep 2019 00:34
URI: https://eprints.lancs.ac.uk/id/eprint/27111

Actions (login required)

View Item View Item