安徽农业大学学报2017，Vol.44Issue(1)：165-170,6.DOI:10.13610/j.cnki.1672-352x.20170208.012
GEP优化的多输出RBF网络作物生理参数建模
Multi output RBF network based on GEP optimization of modeling for crop physiological parameters
摘要
Abstract
In order to address such problems as single output,parameter optimization difficulties,and lack of prediction accuracy etc.in modeling and predicting for the conventional plants based on regression and neural network,A multi output RBF network based on GEP optimization was designed with the help of strong global search ability of GEP and multi output arbitrary nonlinear function approximation of RBF network.Five key environmental factors of rice and tomato served as input,leaf CO2 exchange rate and transpiration rate as output,the proposed method was adopted in modeling and verifying.Experimental results showed:in view of the root mean square error,compared with GA-RBF and RBF,CO2 exchange rate and transpiration rate in rice using the GEP-RBF model were reduced by ～28.4％,38.0％ and 89.9％,62.8％,respectively,while those in tomato were reduced by ～56.9％,48.4％ and 75.3％,67.1％,respectively;on the balance of multiple output result,compared with GA-RBF and RBF,using the GEP-RBF model could improve it by ～16.4％-77.4％.The study indicated that the GEP-RBF model has good prediction accuracy and multi output balance,and it is an effective method for crop growth modeling.关键词
作物模型/基因表达式编程/优化/RBF神经网络Key words
crop model/gene expression programming/optimization/RBF neural network分类
农业科技引用本文复制引用
闵文芳,江朝晖,李婷婷,祁钊,饶元..GEP优化的多输出RBF网络作物生理参数建模[J].安徽农业大学学报,2017,44(1):165-170,6.基金项目
农业部国际科技合作项目(948计划,2015-Z44和2016-X34),安徽省自然科学基金(1508085MF110)和安徽省科技攻关项目(1501031102)共同资助. （948计划,2015-Z44和2016-X34）
