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中国农学通报

中国农学通报 ›› 2013, Vol. 29 ›› Issue (19): 57-61.doi: 10.11924/j.issn.1000-6850.2013-0917

• 林学 园艺 园林 • 上一篇    下一篇

基于LVQ神经网络森林立地类型划分研究

马天晓 王艳梅 尚铁军 黄家荣   

  • 收稿日期:2013-04-01 修回日期:2013-05-10 出版日期:2013-07-05 发布日期:2013-07-05
  • 基金资助:
    河南农业大学博士启动项目;河南省教育厅自然科学研究计划项目

Forest Type Divide Studies on the Basis of Learning Vector Quantization

  • Received:2013-04-01 Revised:2013-05-10 Online:2013-07-05 Published:2013-07-05

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摘要： 传统方法森林立地类型划分是通过对森林立地系统的各因子之间的关系，通过用简化、依赖和间接的数学公式来反映，结果所建立的模型分类与评价效果并不理想。为了解决传统方法的瓶颈，笔者利用LVQ神经网络建模理念，尝试探讨一套新的立地分类方法。结果表明，LVQ神经网络模型对森林立地类型划分结果能较好地反映实际情况，可以为林业经营者营林规划提供参考。

关键词: 序列分析, 序列分析

Abstract: The traditional methods of forest site type classification is to reflect the relationship of the factors between the forest site system by using a simplified, dependent and indirect mathematical formula, as a result of which, the model classification and evaluation of the effect are not satisfactory. In order to solve the bottleneck of traditional methods, the author employed the LVQ neural network modeling concept, aimed to explore a new set of site classification. The results showed that: LVQ neural network model could better reflect the actual situation of the results of the forest site type classification and provide a reference for forestry operators’ silvicultural planning.