Wu, Z.Q. and Harris, C.J.
Neurofuzzy Modelling and State Estimation.
IEEE Medit. Symp. on Control and Automation: Circuits, Systems and Computers '96
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It is of great practical significance to merge the neural network identification technique and the Kalman filter to achieve optimal adaptive filtering and prediction for unknown observable nonlinear processes. In this paper, an operating point dependent ARMA model is used to represent the nonlinear system, and a neurofuzzy network is used to identify this model. It is then converted to its equivalent state-space representation with which a Kalman filter is applied to perform state estimation. Two approaches to combine the neurofuzzy modelling and the Kalman filter algorithm, indirect method and direct method, are presented. A simulated example is also given.
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