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用泛化改进的BP神经网络估测森林蓄积量
引用本文:琚存勇,蔡体久.用泛化改进的BP神经网络估测森林蓄积量[J].林业科学,2006,42(12):59-62.
作者姓名:琚存勇  蔡体久
作者单位:东北林业大学林学院,哈尔滨150040
摘    要:介绍主成分变换和经规则化调整法进行泛化改进的BP神经网络在森林蓄积量建模估测中的应用,比较普通BP神经网络与泛化改进的BP神经网络对蓄积量预报的差异,分析直接用中心标准化的观测值建立仿真模型和进行主成分变换后再建立模型的效率问题.结果表明:泛化改进的BP神经网络比普通BP神经网络具有更高的预报精度,利用主成分得分作为仿真模型的变量比直接用观测值作变量具有更快的速度,并保证了预报精度.

关 键 词:BP神经网络  主成分变换  泛化  森林蓄积量
文章编号:1001-7488(2006)12-0059-04
收稿时间:2006-06-30
修稿时间:2006年6月30日

Forest volume estimate based on Bayesian regularization back propagation neural network
Ju Cunyong,Cai Tijiu.Forest volume estimate based on Bayesian regularization back propagation neural network[J].Scientia Silvae Sinicae,2006,42(12):59-62.
Authors:Ju Cunyong  Cai Tijiu
Institution:Forestry College of Northeast Forestry University Harbin 150040
Abstract:The application of principal component transformation and Bayesian regularization back propagation (BP)neural network in forest volume estimate was introduced through a specific sample in this paper. The difference of forest volume estimate between general back propagation neural network and Bayesian regularization back propagation neural network was compared and the efficiency of estimating forest volume by the means of using original data and transformed data set to establish emulating model was discussed. All the results showed that Bayesian regularization back propagation neural network was more accurate than general BP neural network in estimating forest volume and using transformed data set stemmed from principal component analysis to establish simulating model is more efficient than using original data.
Keywords:BP neural network  principal component transformation  generalization  forest volume
本文献已被 CNKI 维普 万方数据 等数据库收录!
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