CAJ | 학술논문

分别使用基于滑动窗口的VLRBP神经网络模型和基于C-C相空间重构的VLRBP神经网络模型及ARIMA-GARCH模型对欧元汇率时间序列建模和预测,通过比较发现基于C-C相空间重构的VLRBP神经网络对于含有大量非线性成分的欧元汇率时间序列的预测比较准确.同时,为了提高基于滑动窗口的VLRBP网络的泛化性能,提出在训练VLRBP神经网络时应用浴盆曲线方法选取隐层神经元个数和滑动窗口尺寸.
분별사용기우활동창구적VLRBP신경망락모형화기우C-C상공간중구적VLRBP신경망락모형급ARIMA-GARCH모형대구원회솔시간서렬건모화예측,통과비교발현기우C-C상공간중구적VLRBP신경망락대우함유대량비선성성분적구원회솔시간서렬적예측비교준학.동시,위료제고기우활동창구적VLRBP망락적범화성능,제출재훈련VLRBP신경망락시응용욕분곡선방법선취은층신경원개수화활동창구척촌.
It builds a sliding window neural networks model,a neural networks model which is based on phase space reconstruction and an ARIMA-GARCH model,and then the euro foreign exchange rate is forecasted by using the three models.The result shows that the VLRBP neural networks which is based on C-C phase space reconstruction produces better porformance than the other methods in forecasting the euro foreign exchange rate which has a great amount nonlinear components.To improve the generalization performance of the sliding window VLRBP neural networks,it presents a bathtub curve method when searching the size of the hidden neuron and the sliding window of the VLRBP neural networks.