A New Prediction Method for Chaotic Time Series

Juan Du

doi:10.4028/www.scientific.net/AMM.380-384.1673

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 380-384A New Prediction Method for Chaotic Time Series

A New Prediction Method for Chaotic Time Series

Abstract:

In order to show the time cumulative effect in the process for the time series prediction, the process neural network is taken. The training algorithm of modified particle swarm is used to the model for the learning speed. The training data is sunspot data from 1700 to 2007. Simulation result shows that the prediction model and algorithm has faster training speed and prediction accuracy than the artificial neural network.

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Periodical:

Applied Mechanics and Materials (Volumes 380-384)

Pages:

1673-1676

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.380-384.1673

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Online since:

August 2013

Authors:

Juan Du

Keywords:

Artificial Neural Network (ANN), Particle Swarm, Prediction, Time Series, Training

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References

[1] Zhang G P. QiM. Neural Network Forecasting for Seasonal and Trend Time Seriess[J]. European J of Operational Research, 2005, 160(2): 143-146.

Google Scholar

[2] He X G, Xu S H. Process neural network with time varied input and output functions and its applications[J]. Journal of Software, 2003, 14(4): 764-769.

Google Scholar

[3] Xu S H, He X G. Learning algorithms of process neural networks based on orthogonal function basis expansion[J]. Chinese Journal of Computers, 2004, 27(5): 645-650.

Google Scholar

[4] Li Shi-Yong, Li Pan-Chi. Quantum genetic algorithm based on real encoding and gradient information of object function [J]. Journal of HARBIN Institute Of Technology, 2006, 38(8): 1217-1219.

Google Scholar

[5] Shi Y H, Eberhart R C. A modified particle swarm optimizer[C]/IEEE World congress on Computational Intelligence. Piscataway, NJ, USA: IEEE, 1998: 69-73.

DOI: 10.1109/icec.1998.699146

Google Scholar