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Evolving Takagi Sugeno modelling with memory for slow processes.

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

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    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: Article
    Journal or Publication Title: KES Journal: Innovation in Knowledge-Based & Intelligent Engineering Systems
    Uncontrolled Keywords: Evolving Takagi Sugeno ; Fuzzy ; Modelling ; Noise ; Discrete Wavelet Transform
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 27111
    Deposited By: Dr. Plamen Angelov
    Deposited On: 30 Sep 2009 16:30
    Refereed?: Yes
    Published?: Published
    Last Modified: 03 Jun 2014 16:57
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/27111

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