递归神经网络在降雨量预测中的应用研究
发布时间：2018-10-20 15:54
【摘要】：递归神经网络(RNN)模型近年来在许多任务上表现出了优良的性能。运用具有长短期记忆(LSTM)单元的递归神经网络构建模型和通过时间反向传播(BPTT)算法更新网络权重解决长期降雨量的预测问题,较好地解决了高维数、非线性和局部极小问题。选取了前馈神经网络模型(FNN)、小波神经网络(WNN)模型和整合移动平均自回归(ARIMA)模型3种模型进行验证比较。仿真结果表明,递归神经网络模型优于其他模型,训练结果与实际值接近,预测精度较高。预测结果为农业用水管理、合理制定灌溉制度提供了重要的科学依据。
[Abstract]:The recursive neural network (RNN) model has shown excellent performance in many tasks in recent years. Using the recurrent neural network with long and short memory (LSTM) unit to construct the model and renew the network weight through the time back propagation (BPTT) algorithm to solve the long-term rainfall prediction problem, the problems of high dimension, nonlinear and local minimization are well solved. The feedforward neural network model (FNN), wavelet neural network (WNN) model and the integrated moving average autoregressive (ARIMA) model are selected for verification and comparison. The simulation results show that the recurrent neural network model is superior to other models, the training results are close to the actual values, and the prediction accuracy is high. The predicted results provide an important scientific basis for the management of agricultural water use and the rational formulation of irrigation systems.
【作者单位】： 西安交通大学机械制造系统工程国家重点实验室;
【基金】：“十三五”国家重点研发计划(2016YFC0400202)
【分类号】：S161.6;TP183
，
本文编号：2283634
[Abstract]:The recursive neural network (RNN) model has shown excellent performance in many tasks in recent years. Using the recurrent neural network with long and short memory (LSTM) unit to construct the model and renew the network weight through the time back propagation (BPTT) algorithm to solve the long-term rainfall prediction problem, the problems of high dimension, nonlinear and local minimization are well solved. The feedforward neural network model (FNN), wavelet neural network (WNN) model and the integrated moving average autoregressive (ARIMA) model are selected for verification and comparison. The simulation results show that the recurrent neural network model is superior to other models, the training results are close to the actual values, and the prediction accuracy is high. The predicted results provide an important scientific basis for the management of agricultural water use and the rational formulation of irrigation systems.
【作者单位】： 西安交通大学机械制造系统工程国家重点实验室;
【基金】：“十三五”国家重点研发计划(2016YFC0400202)
【分类号】：S161.6;TP183
，
本文编号：2283634
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