NDLI: A study on combined nonlinear dynamic prediction model

Content Provider	IEEE Xplore Digital Library
Author	Ao Shan Zhu Ge Muhammad Khalil Shahid
Copyright Year	2010
Description	Author affiliation: Economics and Management School, Beijing University of Posts and Telecommunications, China (Muhammad Khalil Shahid) \|\| Computer Science and Technology School, Henan Polytechnic University, China (Ao Shan) \|\| School of Information Management, Beijing Information Science & Technology University, China (Zhu Ge)
Abstract	By using a combined nonlinear dynamic system model, a simulation forecast about the sunspot number inflexion for 22nd and 23rd cycle is performed. It is based on a hybrid methodology that combines both Logistic chaotic attraction points and artificial neural network (ANN) models. At first, to calculate the Logistic chaotic attractor according to falling and total solar cycle length for 1610–1986. Aiming at the shortcoming that chaotic time series can not fit the actual fluctuation of small sample discrete data very well, especially for long-term forecast errors, BP neural network is used to predict the fitting errors above, and to correct the final results based on the neural network prediction. The results indicate that prediction accuracy has been improved significantly.
Starting Page	932
Ending Page	935
File Size	223742
Page Count	4
File Format	PDF
ISBN	9781424476169
e-ISBN	9781424476183
DOI	10.1109/ICISE.2010.5689967
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos Training Nonlinear dynamics Logistic map Biological system modeling Time series analysis Neural networks Artificial neural networks Predictive models Sunspot number Logistics
Content Type	Text
Resource Type	Article

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