CAJ | 학술논문

　　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。
　　전통방법삼림입지류형화분시통과대삼림입지계통적각인자지간적관계，통과용간화、의뢰화간접적수학공식래반영，결과소건립적모형분류여평개효과병불이상。위료해결전통방법적병경，필자이용LVQ신경망락건모이념，상시탐토일투신적입지분류방법。결과표명，LVQ신경망락모형대삼림입지류형화분결과능교호지반영실제정황，가이위임업경영자영림규화제공삼고。
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.