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

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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.

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Authors and Affiliations

  1. Centre of Technology and Systems (CTS)-UNINOVA, Monte de Caparica, Portugal

    P. Gil & L. Brito Palma

  2. Electrical Engineering Department, Faculty of Science and Technology, Universidade NOVA de Lisboa, Monte de Caparica, Portugal

    P. Gil, T. Oliveira & L. Brito Palma

  3. CISUC - Centre for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, Coimbra, Portugal

    P. Gil

Authors
  1. P. Gil
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  2. T. Oliveira
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  3. L. Brito Palma
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Correspondence to P. Gil.

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Communicated by V. Loia.

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Gil, P., Oliveira, T. & Palma, L.B. Online non-affine nonlinear system identification based on state-space neuro-fuzzy models. Soft Comput 23, 7425–7438 (2019). https://doi.org/10.1007/s00500-018-3386-4

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  • DOI: https://doi.org/10.1007/s00500-018-3386-4

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