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林业科学 ›› 2006, Vol. 42 ›› Issue (12): 59-62.doi: 10.11707/j.1001-7488.20061210

• 论文及研究报告 • 上一篇    下一篇

用泛化改进的BP神经网络估测森林蓄积量

琚存勇 蔡体久   

  1. 东北林业大学林学院,哈尔滨150040
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2006-12-25 发布日期:2006-12-25

Forest Volume Estimate Based on Bayesian Regularization Back Propagation Neural Network

Ju Cunyong,Cai Tijiu   

  1. Forestry College of Northeast Forestry University Harbin 150040
  • Received:1900-01-01 Revised:1900-01-01 Online:2006-12-25 Published:2006-12-25

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

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.

Key words: BP neural network, principal component transformation, generalization, forest volume