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

被引：57

作者：

de Jesus Rubio, Jose ^{[1
]}

Lughofer, Edwin ^{[2
]}

Meda-Campana, Jesus A. ^{[3
]}

Alberto Paramo, Luis ^{[3
]}

Francisco Novoa, Juan ^{[3
]}

Pacheco, Jaime ^{[1
]}

机构：

[1] Inst Politecn Nacl, ESIME Azcapotzalco, Secc Estudios Posgrado & Invest, Av Granjas 682, Mexico City 02250, DF, Mexico

[2] Johannes Kepler Univ Linz, Dept Knowledge Based Math Syst, Linz, Austria

[3] Inst Politecn Nacl, ESIME Zacatenco, Lab Vibrac & Rotodinam, Mexico City, DF, Mexico

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2018年 / 35卷 / 02期

关键词：

Argument Kalman filter; modeling; fuzzy models; DATA STREAMS; FEEDFORWARD;

D O I：

10.3233/JIFS-18425

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：2585 / 2596

页数：12

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