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中文摘要：
对模型未知非线性系统，将系统输出组成时间序列并通过空间嵌入的方法转化为一个离散动态系统．利用线性AR模型拟合时间序列的线性部分，用神经网络拟合时间序列的非线性部分并补偿外界未知的扰动，提出了通过对状态的观测实现时间序列一步预测的方法．利用滚动优化的思想将一步预测推广，提出了时间序列的N步预测方法，证明了时间序列预测误差有界．通过对预测误差进行概率密度估计和检验，提出了故障的预报方法．对F-16歼击机的结构故障预报结果表明了方法的有效性．
英文摘要：
According to the Takens embedding theorem, the nonlinear time series combined with system output is converted into discrete dynamic system. An autoregressive model is used to fit the linear part of series; the neural network is used to fit the nonlinear part of series and to compensate for the unknown disturbance. The prediction of time series is achieved by the observation of system states. So a one-step prediction method is proposed. Using the so-called moving horizon optimization method, one-step prediction is extended to N-steps prediction. The boundedness of prediction error is proved. The fault is predicted by the prediction error through density function estimation and hypothesis test. The simulation of the structure fault prediction of a fighter F-16 proved the efficiency of the model.
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