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NNSYSID-Toolbox for System Identification with Neural Networks

Author

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  • Magnus Nørgaard
  • Ole Ravn
  • Niels Kjølstad Poulsen

Abstract

The NNSYSID toolset for System Identification has been developed as an add on to MATLAB®. The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. This paper gives an overview of the design of NNSYSID and demonstrates its features in an example.

Suggested Citation

  • Magnus Nørgaard & Ole Ravn & Niels Kjølstad Poulsen, 2002. "NNSYSID-Toolbox for System Identification with Neural Networks," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 8(1), pages 1-20, March.
    • Handle: RePEc:taf:nmcmxx:v:8:y:2002:i:1:p:1-20
    DOI: 10.1076/mcmd.8.1.1.8342
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    Cited by:

    1. Tolo, Silvia & Tian, Xiange & Bausch, Nils & Becerra, Victor & Santhosh, T.V. & Vinod, G. & Patelli, Edoardo, 2019. "Robust on-line diagnosis tool for the early accident detection in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 110-119.
    2. Selimefendigil, Fatih & Bayrak, Fatih & Oztop, Hakan F., 2018. "Experimental analysis and dynamic modeling of a photovoltaic module with porous fins," Renewable Energy, Elsevier, vol. 125(C), pages 193-205.

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