计算机与数字工程2016，Vol.45Issue(7)：1210-1215,6.DOI:10.3969/j.issn.1672-9722.2016.07.004
基于改进PSO优化RBF神经网络的温室温度预测研究
Greenhouse Temperature Forecast Based on Improved PSO for Optimizing RBF Neural Network
摘要
Abstract
Based on the meteorological data and outside greenhouse as input,the greenhouse temperature humidity and other meteorological factors as the output,the prediction model of greenhouse environment temperature and humidity with improved RBF neural network based on improved PSO algorithm.The simulation test and performance evaluation are carried out to verify the feasibility and effectiveness of the proposed method through the experiment.The model is convenient for da-ta acquisition,few parameters and high accuracy,which provides scientific basis for the prediction,regulation and manage-ment of extreme temperature in greenhouse.关键词
RBF神经网络/PSO算法/预测模型/温室Key words
RBF neural network/PSO algorithm/prediction model/greenhouse分类
信息技术与安全科学引用本文复制引用
王媛媛..基于改进PSO优化RBF神经网络的温室温度预测研究[J].计算机与数字工程,2016,45(7):1210-1215,6.基金项目
江苏省高校自然科学研究面上项目(编号:15KJB520004) （编号:15KJB520004）
江苏省先进制造技术重点实验室开放基金(编号:HGAMTL-1401) （编号:HGAMTL-1401）
淮安市应用研究与科技攻关(工业)计划项目(编号:HAG2014028) （工业）
淮安市应用研究与科技攻关计划项目(编号:HAG2015060) （编号:HAG2015060）
淮阴工学院科研基金项目(编号:HGC1412)资助. （编号:HGC1412）
