Non-Autoregressive vs Autoregressive Neural Networks for System Identification

@article{Weber2021NonAutoregressiveVA,
  title={Non-Autoregressive vs Autoregressive Neural Networks for System Identification},
  author={Daniel Weber and Clemens G{\"u}hmann},
  journal={ArXiv},
  year={2021},
  volume={abs/2105.02027}
}
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References

SHOWING 1-10 OF 45 REFERENCES

Comparative study of neural networks for dynamic nonlinear systems identification

TLDR
The simulation results show the superior performance of DRNN identification model over NARX and MLFFNN identification models.

Nonlinear system modeling using convolutional neural networks

  • M. LopezWen Yu
  • Computer Science, Engineering
    2017 14th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)
  • 2017
TLDR
The proposed deep learning methods for dynamic system identification are validated with two benchmark data sets and are divided into two cases: feedforward CNN and backpropagation training.

Deep Convolutional Networks in System Identification

TLDR
This paper establishes connections between the deep learning and the system identification communities and explores the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models.

The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions

  • S. Hochreiter
  • Computer Science
    Int. J. Uncertain. Fuzziness Knowl. Based Syst.
  • 1998
TLDR
The de-caying error flow is theoretically analyzed, methods trying to overcome vanishing gradients are briefly discussed, and experiments comparing conventional algorithms and alternative methods are presented.

Non-linear system identification using neural networks

TLDR
This paper investigates the identification of discrete-time nonlinear systems using neural networks with a single hidden layer using new parameter estimation algorithms derived for the neural network model based on a prediction error formulation.