基于模糊神经网络的温室小气候预测模型  

Prediction model of greenhouse microclimate based on fuzzy neural network

作　　者：何建祥 宋欣 朱垲 宋俊杰 谢周强 He Jianxiang;Song Xin;Zhu Kai;Song Junjie;Xie Zhouqiang(College of Engineering and Technology,Tianjin Agricultural University,Tianjin 300392,China)

机构地区：[1]天津农学院工程技术学院,天津300392

出　　处：《天津农学院学报》2023年第1期74-79,共6页Journal of Tianjin Agricultural University

基　　金：天津市科技局企业科技特派员项目(18JCTPJC67600,19JCTPJC59200);天津农学院研究生科研创新项目(2019XY042)。

摘　　要：为了实现对温室环境的精准控制,针对温室环境调节过程的滞后响应特性,采用模糊神经网络对温室环境因子进行预测。通过确定模糊神经网络的网络结构、隶属度函数和模糊规则等参数,对温室小气候预测系统进行建模。经过小波降噪处理过的数据通过温室小气候预测模型,实现对环境因子的预测。模型验证结果表明采用温度、湿度和光照强度模型得到的相关度均达到了95%以上,具有良好的预测效果,能够为后续温室的控制决策提供有效依据。In order to achieve precise control of the greenhouse environment,a fuzzy neural network was used to predict the greenhouse environment factors in view of the large lagging characteristics of the greenhouse environment.By determining the network structure,membership function and fuzzy rules and other parameters of the fuzzy neural network,the greenhouse microclimate prediction system was modeled.The data processed by wavelet noise reduction was used to predict the environmental factors through the greenhouse microclimate prediction model.The model verification results showed that the correlation degree obtained by using the temperature,humidity and light intensity model has reached more than 95%,which has a good predictive effect and can provide an effective basis for subsequent greenhouse control decisions.

关 键 词：ANFIS 温室环境 预测模型 温室小气候 模糊推理系统 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]