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Nonlinear Dynamic System Identification

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Nonlinear System Identification

Abstract

This chapter addresses many fundamental issues arising when transitioning from nonlinear static to nonlinear dynamic models. Many aspects are very general in nature and independent of the specific model architecture. They are analyzed here. The two competing concepts of external and internal dynamics are contrasted. It is explained how equation and output errors are traditionally treated in the neural network terminology as series-parallel and parallel model structures. The additional difficulties feedback causes are discussed and how they are dealt with in learning as well. A large section discusses the much-neglected topic of suitable excitation signals for nonlinear dynamic processes and new ideas for their analysis. Finally, a new signal generator is proposed that is capable of generating very good excitation signals, which is much more flexible and in contrast to the conventional approach of postulating a specific parameterized signal type.

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Notes

  1. 1.

    This analysis is carried out for the external dynamics approach because it allows us to gain some important insights about the desirable properties of the excitation signals. Although with the internal dynamics approach the one-step prediction function is not explicitly approximated, this analysis based on information content considerations is also valid for this class of approaches.

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  1. University of Siegen, Netphen, Germany

    Oliver Nelles

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  1. Oliver Nelles
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Nelles, O. (2020). Nonlinear Dynamic System Identification. In: Nonlinear System Identification. Springer, Cham. https://doi.org/10.1007/978-3-030-47439-3_19

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