DocumentCode :
1933954
Title :
Time Series Forecasting for Density of Wood Growth Ring using ARIMA and Neural Networks
Author :
Li, Ming-bao ; Zhang, Jia-wei ; Zheng, Shi-Qiang
Author_Institution :
Northeast Forestry Univ., Harbin
Volume :
5
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
2816
Lastpage :
2820
Abstract :
Wood density is one of the most important wood characteristics which determine final wood product qualities and properties. In this article, ARIMA, multilayer perceptron (MLP), and particle swarm optimization BP (PSO-BP) network models are considered along with various combinations of these models for forecasting density of wood growth ring. The forecasting principle and procedure of these three methods are presented. Measurement experiments are carried out to get the time series data of wood density. Simulation comparison of forecasting performances shows that the neural network models with particle swarm optimization give a better performance in solving the wood density forecasting problem.
Keywords :
forecasting theory; multilayer perceptrons; particle swarm optimisation; time series; multilayer perceptron; network models; neural networks; particle swarm optimization; time series forecasting; wood growth ring; Artificial intelligence; Cybernetics; Density measurement; Electronic mail; Forestry; Machine learning; Multilayer perceptrons; Neural networks; Particle swarm optimization; Predictive models; Neural networks; Time series forecasting; Wood density;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370627
Filename :
4370627
Link To Document :
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