Neural network updating via argument Kalman filter for modeling of Takagi-Sugeno fuzzy models

In this article, an argument Kalman filter is exposed for the fast updating of a neural network. The argument Kalman filter is developed based on the extended Kalman filter, but the recommended scheme has the next two advantages: first, it has less computational complexity because it only employs th...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2018-01, Vol.35 (2), p.2585-2596
Main Authors: Rubio, José de Jesús, Lughofer, Edwin, Meda-Campaña, Jesús A., Páramo, Luis Alberto, Novoa, Juan Francisco, Pacheco, Jaime
Format: Article
Language:English
Subjects:
Extended Kalman filter
Kalman filters
Neural networks
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Summary:In this article, an argument Kalman filter is exposed for the fast updating of a neural network. The argument Kalman filter is developed based on the extended Kalman filter, but the recommended scheme has the next two advantages: first, it has less computational complexity because it only employs the Jacobian argument instead of the full Jacobian, second, its gain is ensured to be uniformly stable based on the Lyapunov approach. The commented scheme is applied for the modeling of two Takagi-Sugeno fuzzy models.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-18425