Adaptive Neurofuzzy Kalman Filter
Adaptive Neurofuzzy Kalman Filter
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.
1344-1350
Wu, Z.Q.
fc163085-376c-4f78-9e5a-77c8bc5038ad
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
September 1996
Wu, Z.Q.
fc163085-376c-4f78-9e5a-77c8bc5038ad
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
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.
.
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Conference or Workshop Item
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Abstract
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.
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Published date: September 1996
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Address: New Orlean,USA
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FUZZ-IEEE '96 - Proceedings of the fifth IEEE International Conference on Fuzzy Systems, 1996-08-31
Organisations:
Southampton Wireless Group
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Local EPrints ID: 250002
URI: http://eprints.soton.ac.uk/id/eprint/250002
PURE UUID: 976ea8a4-ed95-4124-8826-89b2ada8a8dd
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Date deposited: 19 Apr 2000
Last modified: 08 Jan 2022 05:41
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Author:
Z.Q. Wu
Author:
C.J. Harris
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