Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/4611
Title: Dynamic reconstruction of chaotic systems from inter-spike intervals using least squares support vector machines
Authors: İplikçi, Serdar.
Keywords: Chaotic time series
Dynamic reconstruction
Least squares support vector machines
Approximation theory
Computer simulation
Least squares approximations
Mathematical models
Time series analysis
Vectors
Chaos theory
Publisher: Elsevier
Abstract: This work presents a methodology for dynamic reconstruction of chaotic systems from inter-spike interval (ISI) time series obtained via integrate-and-fire (IF) models. In this methodology, least squares support vector machines (LSSVMs) have been employed for approximating the dynamic behaviors of the systems under investigation. Higher generalization capability and avoidance of local minima constitute the main reasons behind the choice of LSSVMs as the approximation tool. Simulation results have shown that established LSSVM models possess great potential for the reconstruction of chaotic dynamics; in other words, they are able to estimate some dynamic invariants of the underlying chaotic systems as well as they can accurately predict short-term evolution within the horizon of predictability. Moreover, LSSVM models maintain their reconstruction performance even in the case of the existence of noisy data. © 2006 Elsevier Ltd. All rights reserved.
URI: https://hdl.handle.net/11499/4611
https://doi.org/10.1016/j.physd.2006.02.008
ISSN: 0167-2789
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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