用人工神经网络构建多形地位指数模型  

Constructing Polymorphous Site Index Model with Artificial Neural Network

作　　者：高光芹[1] 郭芳[1] 黄家荣[1] 

机构地区：[1]河南农业大学,河南郑州450002

出　　处：《西部林业科学》2014年第4期101-105,共5页Journal of West China Forestry Science

基　　金：河南省科技攻关项目(0624050007);河南农业大学博士基金项目(30400242)

摘　　要：以马尾松人工林132株优势木树干解析数据为训练样本,用145块标准地优势木平均高数据为检验样本,把林分年龄和地位指数或优势木平均高作为输入变量,将优势木平均高或地位指数作为输出变量,通过构建人工神经网络逆模型的途径,分别建立了多形地位指数曲线式和计算式模型。结果表明,多形地位指数曲线式的总体拟合精度为99.64%,总体预测精度达96%以上,比传统技术构建的多形地位指数模型能较真实地模拟各地位级的多形曲线;多形地位指数计算式的总体拟合精度为98.81%,用于计算地位指数,省去用迭代法计算地位指数的工作量。基于BP神经网络模型多形地位指数模型,对马尾松人工林地位指数测定提供指导作用,可为森林立地质量评价提供理论依据。By means of constructing inverse model of artificial neural network , the polymorphic site index curve and calculation formula were established with stem analysis data of 132 dominant trees of Pinus massoniana planta-tion as training samples , and with average height of 145 dominant trees from standard field as tested samples.The stand age and site index or average high of dominant tree was selected as input variables , and the average height of dominant tree or site index was regarded as output variables , The results showed that the overall accuracy of the in-dex curve was 99.64 %, the overall prediction accuracy reached 96 %, and it could precisely simulate polymor-phic curve than the polymorphic site index model constructed through traditional technology.The overall accuracy of the calculation formula was 98.81 %, and it could reduce workload to calculate position index by using iterative method.

关 键 词：马尾松人工林 多形地位指数 人工神经网络 逆模型 

分 类 号：S758.57[农业科学—森林经理学]