江西农业大学学报Issue(5)：984-989,6.
基于BP神经网络的林木资源资产批量评估模型优化
Optimization of Mass Appraisal Model for Forest Resource Assets Based on the BP Neural Network
摘要
Abstract
Mass appraisal is of high efficiency,high precision,low cost,satisfies the needs of vast-amount evaluation.In this study,BP neural network was applied to mass appraisal of mid-age forest assets evaluation. By comparing different learning algorithms and the numbers of hidden layer nodes,selecting layer factors,using sensitivity analysis method which revealed the factors’ influence degree to the assessed value,the model struc-ture of BP neural network was optimized.The results showed that Bayesian regularization method was better than L-M algorithm;the contribution to the assessed values of the four factors including age,rate,accumula-tion,tree species was more than 60%;the best model structure was BR9-10-1.Its mean absolute error was 32.46 yuan/hm2 ,mean absolute percentage error was 1.28%,and decision coefficient was 0.999 7.The model has high fitting accuracy and generalization ability thus meets the requirement of mass appraisal of mid-age forest resource assets.关键词
林木资源资产/批量评估/BP神经网络/敏感性分析Key words
forest assets evaluation/mass appraisal/BP neural network/sensitivity analysis method分类
管理科学引用本文复制引用
吕丹,郑世跃,欧阳勋志,郭孝玉..基于BP神经网络的林木资源资产批量评估模型优化[J].江西农业大学学报,2014,(5):984-989,6.基金项目
国家自然科学基金（31160159，31360181）和高等学校博士学科点专项科研基金博导类资助课题 （）
