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- W2372554270 abstract "The nonlinear time series, which is formed by the output of the unknown system, is converted into discrete time-varying dynamic system with space reconstruction. A novel method is presented for the prediction of time series which is achieved by the adaptive observation of system states. An AR model is used to approximate the linear part of the time series; the nonlinear part and the approximate error are regarded as the unknown-input of system. A one-step-ahead prediction method is proposed. The one-step-ahead prediction is then extended to N-steps-ahead moving horizon prediction, and the prediction error is proved bounded. The fault is predicted conveniently by the prediction of unknown-input and prediction error. The simulation results show the method is efficient." @default.
- W2372554270 created "2016-06-24" @default.
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- W2372554270 date "2005-01-01" @default.
- W2372554270 modified "2023-09-23" @default.
- W2372554270 title "Fault Prediction for Nonlinear Time Series Based on Unknown Input Observer" @default.
- W2372554270 hasPublicationYear "2005" @default.
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