Title:Neural network updating via argument Kalman filter for modeling of Takagi-Sugeno fuzzy modelsAuthor(s):Jose de Jesus Rubio, Edwin Lughofer, Jesus A. Meda-Campana, Luis-Alberto Paramo, Juan J. Novoa, Jaime PachecoAbstract: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.Journal:Journal of Intelligent and Fuzzy SystemsPublisher:IOS PressISSN:1875-8967Page Reference:page 2585-2596, 11 page(s)Publishing:2018Volume:35Number:2