The NNSYSID toolbox - a MATLAB toolbox for system identification with neural networks. 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.

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  1. Bueno-Crespo, Andrés; García-Laencina, Pedro J.; Sancho-Gómez, José-Luis: Neural architecture design based on extreme learning machine (2013) ioport
  2. Hametner, Christoph; Jakubek, Stefan: Local model network identification for online engine modelling (2013) ioport
  3. Selimefendigil, F.; Föller, S.; Polifke, W.: Nonlinear identification of unsteady heat transfer of a cylinder in pulsating cross flow (2012)
  4. Gorissen, Dirk; Couckuyt, Ivo; Laermans, Eric; Dhaene, Tom: Pareto-based multi-output metamodeling with active learning (2009)
  5. Messai, Nadhir; Riera, Bernard; Zaytoon, Janan: Identification of a class of hybrid dynamic systems with feed-forward neural networks: about the validity of the global model (2008)
  6. Kotta, Ü.; Chowdhury, F. N.; Nõmm, S.: On realizability of neural networks-based input--output models in the classical state-space form (2006)
  7. Becerra, Victor M.; Galvão, Roberto K. H.; Abou-Seada, Magda: Neural and wavelet network models for financial distress classification (2005) ioport
  8. Corani, Giorgio: An application of pruning in the design of neural networks for real time flood forecasting (2005) ioport
  9. Sadjadian, H.; Taghirad, H. D.: Comparison of different methods for computing the forward kinematics of a redundant parallel manipulator (2005) ioport
  10. Wu, Zhi Qiao; Harris, Chris J.: A neurofuzzy network structure for modelling and state estimation of unknown nonlinear systems (1997)