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

Jose de Jesus Rubio; Edwin Lughofer; Jesus A. Meda-Campana; Luis-Alberto Paramo; Juan J. Novoa; Jaime Pacheco

doi:10.3233/JIFS-18425

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

Jose de Jesus Rubio
, Edwin Lughofer
, Jesus A. Meda-Campana
, Luis-Alberto Paramo
, Juan J. Novoa
, Jaime Pacheco

Institute for Mathematical Methods in Medicine and Data Based Modeling

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	2585-2596
Number of pages	11
Journal	Journal of Intelligent and Fuzzy Systems
Volume	35
Issue number	2
DOIs	https://doi.org/10.3233/JIFS-18425
Publication status	Published - 2018

Fields of science

101 Mathematics
101013 Mathematical logic
101024 Probability theory
102001 Artificial intelligence
102003 Image processing
102019 Machine learning
603109 Logic
202027 Mechatronics

JKU Focus areas

Computation in Informatics and Mathematics
Mechatronics and Information Processing
Nano-, Bio- and Polymer-Systems: From Structure to Function

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10.3233/JIFS-18425

Cite this

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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.",

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AU - Rubio, Jose de Jesus

AU - Lughofer, Edwin

AU - Meda-Campana, Jesus A.

AU - Paramo, Luis-Alberto

AU - Novoa, Juan J.

AU - Pacheco, Jaime

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

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U2 - 10.3233/JIFS-18425

DO - 10.3233/JIFS-18425

M3 - Article

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EP - 2596

JO - Journal of Intelligent and Fuzzy Systems

JF - Journal of Intelligent and Fuzzy Systems

IS - 2

ER -

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

Abstract

Fields of science

JKU Focus areas

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