【摘要】 对相关参数预测的非线性时间序列进行分析 ,提出了用相关参数实现多参数、多步数预测的神经网络方法 ,建立了神经网络实时预测模型 ,并对预测参数进行了综合处理 ,不仅弥补了预测信息的不足 ,而且能够使故障状态提前得到多次警报 ,克服了传统预测方法所遇到的困难 ,提高了预测模型精度更多还原

【Abstract】 With analysing the non linear time series for correlative parameters prediction, a neural networks method to predict the multi parameters with multi steps using correlative parameters has been demonstrated and a reat time neural networks predicting model has also been built in this paper. The prediction results were dealt with comprehensively. The method not only can make up for the lack of original information, but also can give multiple alarms for a failure occurrence ahead of time. The problems which the traditional prediction methods often meet are solved and also the precision of prediction model is improved.更多还原