The use of ARIMA models for reliability forecasting and analysis
Proceedings of the 23rd international conference on on Computers and industrial engineering
Parzen-Window Network Intrusion Detectors
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Direct adaptive NN control of a class of nonlinear systems
IEEE Transactions on Neural Networks
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As a representative complex system, the aircraft modeled very difficultly and imprecisely. This makes the model-based fault detection methods degenerated. In this dissertation, the nonlinear time series, which is constructed by output variables of aircraft, is converted into discrete dynamic system, and then a novel series prediction method is achieved by the adaptive observation of system states. An online adaptive RBFNN is used to fit the nonlinearity of system and to compensate the unknown disturbance. Thereby a one-step-ahead prediction method is proposed. By using probability density estimation and hypothesis testing for the observation error, the fault is detected directly. Finally, a rule-table is established for fault identification. The results of simulation prove the method's efficiency.