期刊
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
卷 35, 期 2, 页码 2585-2596出版社
IOS PRESS
DOI: 10.3233/JIFS-18425
关键词
Argument Kalman filter; modeling; fuzzy models
资金
- Austrian COMET-K2 programme of the Linz Center of Mechatronics (LCM) - Austrian federal government
- federal state of Upper Austria
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.
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