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

Article type: Research Article

Authors: Rubio, José de Jesús^{a; *} | Lughofer, Edwin^b | Meda-Campaña, Jesús A.^c | Páramo, Luis Alberto^c | Novoa, Juan Francisco^c | Pacheco, Jaime^a

Affiliations: [a] Sección de Estudios de Posgrado e Investigación, ESIME Azcapotzalco, Instituto Politécnico Nacional, Av. de las Granjas no.682, Col. Santa Catarina, México D.F., 02250, México | [b] Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Austria | [c] Laboratorio de Vibraciones y Rotodinámica, ESIME Zacatenco, Instituto Politécnico Nacional, México

Correspondence: [*] Corresponding author. José de Jesús Rubio, Sección de Estudios de Posgrado e Investigación, ESIME Azcapotzalco, Instituto Politécnico Nacional, Av. de las Granjas no.682, Col. Santa Catarina, México D.F., 02250, México. E-mail: [email protected].

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.

Keywords: Argument Kalman filter, modeling, fuzzy models

DOI: 10.3233/JIFS-18425

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 2585-2596, 2018