摘要
为了解决由多层前馈神经网络递推运算获得的多步预报存在的预报误差迭代累积问题 ,提出了基于局部递归神经网络的多步递归神经网络 (MSRN)模型 ,对时间序列进行了多步预报 .用模拟振动数据把MSRN模型用作单步和多步的预报能力 ,同经典的多层前馈神经网络进行了比较 ,并预报了天津石化总公司炼油厂大机组某测点振动的变化趋势 实践表明 ,用该方法进行多步预报误差小 ,并具有良好的预报能力 .
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.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2002年第7期722-725,756,共5页
Journal of Xi'an Jiaotong University
