Position: Home > Articles > Water Demand Prediction for Walnut Fruit during Swelling Period Based on MIV-MEA-Elman Neural Network
Water Saving Irrigation
2020
(4)
68-72
基于MIV-MEA-Elman神经网络的核桃果实膨大期需水量预测
作 者：
邓皓;李文竹;刘婧然;刘心
单 位：
河北工程大学信息与电气工程学院
关键词：
核桃需水;Elman神经网络;思维进化算法;MIV算法
摘 要：
核桃作物需水规律错综复杂,与气温、气压、相对湿度等因子之间具有复杂的非线性关系。针对这种特点,结合河北邯郸试验田核桃需水的历史数据和气象数据,提出了一种MIV-MEA-Elman模型。经MIV(平均影响值)算法筛选,得出4个较优的指标:平均气温、平均气压、相对湿度、日照时数。以此作为经MEA(思维进化算法)优化后的Elman神经网络模型的输入,核桃逐日需水量作为输出,经过对仿真结果分析,此模型的均方根误差为0.317,证明具有良好的预测效果。
译 名：
Water Demand Prediction for Walnut Fruit during Swelling Period Based on MIV-MEA-Elman Neural Network
作 者：
DENG Hao;LI Wen-zhu;LIU Jing-ran;LIU Xin;School of Information &Electrical Engineering,Hebei University of Engineering;
关键词：
water demand of walnut;;Elman neural network;;mind evolutionary algorithm;;MIV algorithm
摘 要：
The water requirement of walnut crops is intricate and complicated, and has a complex non-linear relationship with factors such as temperature, pressure, and relative humidity. In view of this characteristic, combined with the historical and meteorological data of walnut water demand in Handan test field, Hebei Province, a MIV-MEA-Elman model was proposed. After screening by the MIV(Mean Impact Value) algorithm, four better indicators, including average temperature, average pressure, relative humidity, and hours of sunshine, were obtained. Then taking them as the input and the daily water demand of walnuts as the output of the Elman neural network model optimized by the MEA(Mind Evolutionary Algorithm), a simulation was conducted. Through the analysis of the simulation results, it was found that the root mean square error of this model was 0.317, which proved that it had a good prediction effect.
导出引用
计量
文章访问数: 18
HTML全文浏览量: 0
PDF下载量: 0
所属期刊
