摘要
应用传统统计技术常会因样本小和测量数据不符某种分布而受到限制．本文评价一种前馈型神经网络算法以预测落叶阔叶林产量．另外，还介绍一种由定性变为定量的数据变换方法，以用相对小的样本建立多元回归预测模型．数据变换方法有助于改善多元回归模型的预测效果．在本实验的条件下。
Use of traditional
statistical techniques is often limited by shortage of observation samples and difference in data
measurement scales. Neural network techniques have been extensively explored in many
fields for prediction and classification as an alternative to statistical methods. In this paper, a
feed forward neural network algorithm for predicting hardwood yield is introduced and
evaluated. In addition, we report a data transformation method developed for converting
qualitative variable data to quantitative data for use in multiple regression when relatively few
samples are available for building prediction models. The method that converts qualitative data
into quantitative data is helpful to improve hardwood yield prediction accuracy by multiple
linear regression models. In this study, the best prediction results using the neural network
technique are obtained.
出处
《应用生态学报》
CAS
CSCD
1999年第2期129-134,共6页
Chinese Journal of Applied Ecology
基金
国家杰出青年科学基金B类资助项目
美国加州IHRMP资助项目
关键词
神经网络
多元回归
森林产量
预测
数据变换
Neural network, Multiple regression, Forest yield prediction,
Data tranformation.[
