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A new method to generate excitation signals for the identification of nonlinear dynamic processes is introduced. The objective of the optimization is a uniform data point distribution in the input space of the nonlinear approximator. This optimization of the excitation signal is passive, thus the whole signal is optimized prior to the measurement of the process and no online adaptation is performed...
A crucial part during the generation of nonlinear dynamic models is the determination of an appropriate model order. Five automated order determination strategies are compared. One model-based and four model-free approaches are investigated. We evaluated the performances of all methods with four artificial test processes and two noise levels. In an external dynamics approach, local model networks...
The problem of identifying nonlinear dynamic systems with one or more inputs and one output (SISO/MISO) is considered. The properties of orthonormal basis functions (OBF) as inputs for a nonlinear approximator are compared to the common ARX structure which possesses both, delayed input and output values, entering the nonlinearity. The disadvantage of optimizing the pole in the OBF context is overcompensated...
This paper introduces the Extended Deterministic Local Search (EDLS) algorithm for Latin Hypercube (LH) designs. The main goal of the algorithm is to improve an existing algorithm towards a better uniformity of the data distribution, while maintaining a good computational performance. After presenting background information about LH designs and how to assess their quality (choice of loss function),...
In this paper five different model free input selection approaches are summarized. While all the criteria are based on geometrical properties of the data, it is possible to visualize most of the approaches in the here called 'Distance Space'. The five discussed approaches for input selection are interpreted and analyzed in the Distance Space scatter plot. The influence of noise and wrong chosen inputs...
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