基于神经网络的混沌时间序列土壤墒情预测预报

土壤墒情预测预报对农业生产、水分循环的研究具有重要的意义。应用混沌理论对具有混沌特性的土壤墒情时间序列进行相空间重构，利用神经网络对土壤墒情时间混沌序列重构相空间中相点的演化过程进行了学习、训练及预测。结果表明，该方法所需的参数较少，简单易行，即只需要土壤墒情时间序列数据。通过对预测预报值与实测数据进行比较，证实了该方法相对误差较小，预测精度高，有一定的可靠性和实用性。

Prediction of Chaotic Soil Moisture Time Series Based on Artificial Neural Network

The prediction of soil moisture is significant to the research on agricultural production and water cycles.Artificial neural network is used to approximate the phase space reconstruction of chaotic soil moisture time series and the future soil moisture was then predicted.Results show that this method is easier to be used in practice because it only needs one parameter-soil moisture time series.The comparison between the predicted value and the measured value indicates that the prediction method has a little...