基于改进遗传神经网络算法的农业土壤测土分类研究  

Agricultural Soil Testing and Classification Based on the Improved Genetic Neural Network Algorithm

作　　者：王文亮[1] 冀文超[1] 

机构地区：[1]河北北方学院图书馆,河北张家口075000

出　　处：《安徽农业科学》2011年第13期8084-8085,共2页Journal of Anhui Agricultural Sciences

基　　金：河北自然科学基金项目(403163)

摘　　要：为解决化肥施用利用率低的问题,需要对土壤等级进行精准定位。传统的神经网络存在不适用于土壤等级精确判定的一些缺陷,构建了基于改进自适应遗传算法的SFAM模型,将其应用于测土分类中,取得了较好的效果。In order to deal with the low utilization rate of chemical fertilizer,the soil needs to be precisely classified.Traditional neural network is not applicable to the precise judgements of soil classification,SFAM model is constructed based on improved adaptive genetic algorithm,and then applied to the soil testing and classification,which achieved good results.

关 键 词：遗传神经网络算法 测土分类 精确农业 

分 类 号：F304.3[经济管理—产业经济]