Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1136
Title: A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity
Authors: Xie, Jing-xin
Cheng, Chuntian
Chau, Kwok-wing
Pei, Yong-zhen
Subjects: Time-delay neural network
Adaptive time-delay neural network
Multiple-neural-network
Multi-step-ahead prediction
Single step iteration
Characteristics decomposition
Spline interpolation
Issue Date: 2006
Publisher: Inderscience
Source: International journal of environment and pollution, 2006, v. 28, no. 3/4, p. 364-381.
Abstract: The availability of accurate empirical models for multi-step-ahead (MS) prediction is desirable in many areas. Some ANN technologies, such as multiple-neural network, time-delay neural network (TDNN), and adaptive time-delay neural network (ATNN), have proven successful in addressing various complicated problems. The purpose of this study was to investigate the applicability of neural network MS predictive models. Motivated by the above-mentioned technologies, we proposed a hybrid neural network model, which integrated characteristics decomposition units, and a dynamic spline interpolation unit into the multiple ATNNs. Inside the net, the regular and certain information were extracted to ATNN, while both time delays and weights were dynamically adapted. The yearly average of the sunspots, which has been considered by geophysicists, environment scientists, and climatologists as a complicated non-linear system, was selected to test the hybrid model. Comparative results were presented between a traditional MS predictive model based on TDNN and the proposed model. Validation studies indicated that the proposed model is quite effective in MS prediction, especially for single-factor time series.
Rights: Copyright © 2006 Inderscience Enterprises Ltd. The journal web page at: http://www.inderscience.com/browse/index.php?journalID=9.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/1136
DOI: 10.1504/IJEP.2006.011217
ISSN: 0957-4352 (print)
1741-5101 (online)
Appears in Collections:CEE Journal/Magazine Articles

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