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An Approach to Robust Fault Detection for Nonlinear System Based on RBF Neural Network Observer 

Hu Shousong\ and\ Zhou Chuan (Department of Automatic Control, Nanjing University of Aeronautics and Astronautics·Nanjing,210016,P.R.China) Hu Weili\ and\ Chen Qingwei (Department of Automatic Control, Nanjing University of Science and Technology·Nan  
A new robust fault detection and isolation (FDI) method based on neural network observer is presented for a class of affine nonlinear dynamic system. A radial basis function neural network is used to approximate the nonlinear item of the monitored system to improve the accuracy of state estimation, and the state estimation error is proved to be zero asymptotically. On the other hand, a new index of weight tuning is adopted to improve the robustness of neural network fault classifier for the modelling error and disturbance.
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