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Online non-affine nonlinear system identification based on state-space neuro-fuzzy models

  • P. Gil
  • T. Oliveira
  • L. Brito Palma
Methodologies and Application
  • 20 Downloads

Abstract

This paper proposes a new general recurrent state-space neuro-fuzzy model structure. Three topologies are under assessment, including the state-input recurrent neuro-fuzzy system, the series-parallel recurrent neuro-fuzzy system and the parallel recurrent neuro-fuzzy system. Moreover, the underlying generalised state-space Takagi–Sugeno system is proven to be a universal approximator, and some stability conditions derived for this system. The online training is carried out based on a constrained unscented Kalman filter, where weights, membership functions and consequents are recursively updated. Results from experiments on a benchmark MIMO system demonstrate the applicability and flexibility of the proposed system identification approach.

Keywords

Nonlinear system identification Takagi–Sugeno models Neuro-fuzzy systems Unscented transform Kalman filter 

Notes

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflicts of interest.

Ethical approval

This article does no contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Centre of Technology and Systems (CTS)-UNINOVAMonte de CaparicaPortugal
  2. 2.Electrical Engineering Department, Faculty of Science and TechnologyUniversidade NOVA de LisboaMonte de CaparicaPortugal
  3. 3.CISUC - Centre for Informatics and Systems of the University of CoimbraUniversidade de CoimbraCoimbraPortugal

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