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Bibliographic record number: 314955

Journal

Authors: Liatsis, Panos; Foka, Amalia; Goulermas, John Yannis; Mandić, Lidija
Title: Adaptive Polynomial Neural Networks for Times Series Forecasting
Source: Proceedings ELMAR-2007 / Grgić, M ; Grgić, S. (ed). - Zagreb : Croatian Society Electronics in Marine-ELMAR, Zadar , 2007. 35-39 (ISBN: 978-953-7044-05-3).
ISSN: 1334-2630
Meeting: 49th International Symposium ELMAR-2007
Location and date: Zadar, Hrvatska, 12-14.09.2007.
Keywords: Genetic Algorithms; polynomial neural networks; time series; forecasting
Abstract:
Time series prediction involves the determination of an appropriate model, which can encapsulate the dynamics of the system, described by the sample data. Previous work has demonstrated the potential of neural networks in predicting the behaviour of complex, non-linear systems. In particular, the class of polynomial neural networks has been shown to possess universal approximation properties, while ensuring robustness to noise and missing data, good generalisation and rapid learning. In this work, a polynomial neural network is proposed, whose structure and weight values are determined with the use of evolutionary computing. The resulting networks allow an insight into the relationships underlying the input data, hence allowing a qualitative analysis of the models´ performance. The approach is tested on a variety of non-linear time series data.
Type of meeting: Predavanje
Type of presentation in a journal: Other
Type of peer-review: International peer-review
Project / theme: 036-0361630-1635, 128-1281957-1958
Original language: ENG
Category: Znanstveni
Research fields:
Electrical engineering
Contrib. to CROSBI by: netko (lidija.mandic@grf.hr), 18. Pro. 2007. u 13:25 sati



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