CAJ | 학술논문

基于时间序列预测模型及BP神经网络,提出了新的组合预测方法.该方法采用三层结构的BP神经网络来构造组合预测模型,运用时间序列模型预测方法得出的预测结果,采用历史滚动法将前5年的预测结果数据作为BP网络的输入,以当前年份的预测结果为网络期望输入,建立了ARIMA-ANN组合预报模型.利用Matlab7神经网络工具箱对塔里木河上游源流卡群水文站的年径流量进行了预报及验证.结果表明:组合模型的预报结果精度高,容错能力强,是中长期径流预报的有效方法.
기우시간서렬예측모형급BP신경망락,제출료신적조합예측방법.해방법채용삼층결구적BP신경망락래구조조합예측모형,운용시간서렬모형예측방법득출적예측결과,채용역사곤동법장전5년적예측결과수거작위BP망락적수입,이당전년빈적예측결과위망락기망수입,건립료ARIMA-ANN조합예보모형.이용Matlab7신경망락공구상대탑리목하상유원류잡군수문참적년경류량진행료예보급험증.결과표명:조합모형적예보결과정도고,용착능력강,시중장기경류예보적유효방법.
Based on the time series forecasting model and BP neural network, this paper put forward a new combination forecasting method. A new hybrid approach combined by BP Neural Network and ARIMA model were proposed which were constructed by a three-layer structure. This hybrid approach obtained the absolute errors by ARIMA model firstly, then uses the past 5 years data as input values, and uses the BP neural network to simulate the result by a rolling method, finally established the prediction model by the ARIMA with ANN. By assisting the softs of SPSS13 and Matlab7, this paper applies the combination model to predict the annual runoff of the Kaqun hydrological station.The result indicates that the proposed model has higher forecasting accuracy and more tolerant ability. It is an effective model for runoff prediction of medium to long-term.