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

José De Jesús Rubio, Edwin Lughofer, Jesús A. Meda-Campaña, Luis Alberto Páramo, Juan Francisco Novoa, Jaime Pacheco

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

© 2018 - IOS Press and the authors. All rights reserved. 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.
Original languageAmerican English
Pages (from-to)2585-2596
Number of pages2325
JournalJournal of Intelligent and Fuzzy Systems
DOIs
StatePublished - 1 Jan 2018

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Takagi-Sugeno Fuzzy Model
Kalman filters
Kalman Filter
Updating
Neural Networks
Neural networks
Extended Kalman filters
Modeling
Computational complexity
Lyapunov
Computational Complexity

Cite this

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Neural network updating via argument Kalman filter for modeling of Takagi-Sugeno fuzzy models. / Rubio, José De Jesús; Lughofer, Edwin; Meda-Campaña, Jesús A.; Páramo, Luis Alberto; Novoa, Juan Francisco; Pacheco, Jaime.

In: Journal of Intelligent and Fuzzy Systems, 01.01.2018, p. 2585-2596.

Research output: Contribution to journalArticle

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