摘要
针对现有作物生长建模方法存在的不足,引入基因表达式编程算法,开展番茄叶片CO_2交换速率与主要环境因子关系的建模和预测研究。采用基因表达式编程算法建立番茄叶片CO_2交换速率模型,并将该模型与经典的回归模型及神经网络模型进行预测性能比较。经过3组数据的实验和对比,结果显示,基因表达式编程模型具有最高的预测精度和最佳的预测时效性,同时该算法的复杂度与神经网络相当。研究表明,基因表达式编程算法是一种性能良好的作物建模工具,可作为现有方法的补充。
In order to overcome the shortcomings of existing methods in crop growth modeling,gene expression programming（ GEP） was introduced and adopted in modeling and prediction of tomato leaf CO_2 exchange rate response to major environmental factors. A new model was established by GEP in this paper,then the performance of the proposed model was compared with two classical modeling methods-regression and neural network. The experimental results on three sets of data showed that,the GEP based model get the highest predictive accuracy and the best predictive time effect,at the same time,the complexity of the GEP based model was numerically similar to neural network.The study indicated that GEP is a good tool in crop modeling,and will be an important supplement for the existing methods.
出处
《浙江农业学报》
CSCD
北大核心
2016年第9期1616-1623,共8页
Acta Agriculturae Zhejiangensis
基金
安徽省自然科学基金(1508085MF110)
安徽省科技攻关项目(1501031102)
农业部国际科技合作项目(948计划,2015-Z44)
关键词
作物生长模型
基因表达式编程
CO2交换率
环境因子
crop growth model
gene expression programming
carbon dioxide exchange rate
environmental factors
