Multi-Step Forecasting Method Ｂased on Recurrent Neural Networks Model
Wen Guangrui，Qu Liangsheng，Zhang Xining
(School of Mechianical Engineering,Xi'an　Jiaotong　University,Xi'an 710049,China)

Abstract：In order to solve the problem of the traditional feedforward neural networks with a long-term prediction, an alternative neural model, Multi-step Recurrent Neural Model (MSRN), based on a partially recurrent neural network is proposed. For the recurrent model, a learning phase with the purpose of long-term prediction is imposed, which allows to obtain better predictions of time series in future. In order to validate the performance of the recurrent neural model to predict the dynamic behavior of the series in the future, two different data time series have been used. An artificial data time series and the vibration data measured from real time series are used to compare the ability of multi-step prediction. The results show that the MSRN model can confribute to a good accuracy of prediction.
Keywords：multi-step prediction；neural networks；time series