Comparison Study on Forecasting of Timber Growth Ring Density with SVM and Neural Networks

摘要：

This paper made a comparison study on the forecasting of timber growth ring density with support vector machine (SVM) and radial basis function (RBF) neural network. The objective of this paper is to examine the feasibility of SVM in wood density forecasting by comparing it with a RBF neural network. Wood experiments are carried out to get the data sets. The simulation example shows that SVM outperforms the RBF neural network based on the criteria of normalized mean square error (NMSE), mean absolute error (MAE) and directional symmetry. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast wood density time series.

作者: Mingbao LI Jiawei ZHANG Shiqiang ZHENG

作者单位: Northeast Forestry University, China Beijing University of Aeronautics and Astronautics, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）