Adaptive Neurofuzzy Kalman Filter

Wu, Z.Q. and Harris, C.J. (1996) Adaptive Neurofuzzy Kalman Filter. FUZZ-IEEE '96 - Proceedings of the fifth IEEE International Conference on Fuzzy Systems , 1344-1350.


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It is of great practical significance to merge the neural network identification technique and the Kalman filter to achieve adaptive and optimal 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 approximate each AR parameter of such a model which can then be converted to its equivalent state-space representation. Using this state-space form, a Kalman filter can be applied to estimate the system state. The system modelling algorithm and the Kalman filter are combined in a bootstrap scheme, in which the error between the measured output and the filtered output is used to train the neural network, thus adaptive filtering for noisy nonlinear system is achieved. A simulated example is also given.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Address: New Orlean,USA
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Southampton Wireless Group
ePrint ID: 250002
Date :
Date Event
September 1996Published
Date Deposited: 19 Apr 2000
Last Modified: 31 Mar 2016 13:49
Further Information:Google Scholar

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