本文提出了基于小波网络的非线性时间序列预报模型，探讨了非线性时间序列预报在故障预报中的应用。
Wavelet network based nonlinear time series prediction model is submitted, and nonlinear time series prediction and its application in fault prediction are discussed in this paper.
本文将讨论综合运用非线性回归模型和时间序列分析的方法进行变形预报。
This article demonstrates that deformation forecast will be performed by a comprehensive method of non linear regression model combined with time series analysis.
特别针对模型未知的非线性系统，研究了时间序列分析和神经网络相结合的故障预报方法。
The methods, which combine time series analysis and neural networks, are especially studied and applied in the model-unknown nonlinear system.
采用非线性混沌时间序列预报，不同于传统的时间序列分析方法。
It differs from traditional time-sequence analysis methods in which nonlinear chaotic time-sequence prediction is used.
基于该网络的时间序列预测模型可以实现性能优越的非线性预报器，将其应用于非线性系统的故障预报能够取得良好的效果。
The LMBP neural network can predict nonlinear time series very well and the new method is effective for the fault prediction of nonlinear systems.
基于该网络的时间序列预测模型可以实现性能优越的非线性预报器，将其应用于非线性系统的故障预报能够取得良好的效果。
The LMBP neural network can predict nonlinear time series very well and the new method is effective for the fault prediction of nonlinear systems.
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