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 journalArticlepeer-review

60 Scopus citations

Abstract

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 languageEnglish
Pages (from-to)2585-2596
Number of pages12
JournalJournal of Intelligent and Fuzzy Systems
Volume35
Issue number2
DOIs
StatePublished - 2018

Keywords

  • Argument Kalman filter
  • fuzzy models
  • modeling

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