Forecasting Relative Tree Growth Based on PPR Artificial Neural Network Model

摘要：

Relative growth is defined as the description of the growth relationship between biological and biological part (organ).The relative growth function is a function on variable of growth and non-time variables.In the paper,a system relative growth model which taken Betula costata as an example was constructed based on the projection pursuit regression (PPR)artificial neural network.The system model was constructed in order to predict the tree growth.The results show that the artificial neural network model were available for prediction of the tree growth;the predication average relative error of diameter growth was 0.04,the predication average relative error of height was 0.06,the predication average relative error of volume was 0.12.The PPR model was available for tree growth predication which can be applied to predict the tree growth.

关键词： Relative growth model PPR artificial neural network mode Betula costata

作者: NING Yang-cui ZHENG Xiao-xian LIU Dong-lan ZHAO Jing KONG Ling-hong

作者单位: The Key Laboratory for Silviculture and Conservation of Ministry of EducationBeijing Forestry Univer The Key Laboratory for Silviculture and Conservation of Ministry of Education Beijing Forestry Unive

会议类型: 国际会议

会议名称: 2010 3rd International Conference on Advanced Computer Theory and Engineering(2010年第三届先进计算机理论与工程国际会议 ICACTE 2010)

会议地点: 成都

会议语种:英文

页码: 1-3

在线出版日期: 2010-08-20（万方平台首次上网日期，不代表论文的发表时间）