Abstract
This chapter deals with applications of the input selection strategies introduced in Chapter 15. First, a static process whose characteristics are well-known is studied. Some variables influence the output in a linear way, others in nonlinear ways. The sophisticated local model network-based input selection strategy successfully assigned the linear variable to the consequent space only, while the nonlinear variables are assigned to the premise space as well. This is in perfect alignment with the best-case scenario. This theoretically optimal result was achieved based on the information in the data only. Second, the efficiencies of two fans were modeled by computational fluid dynamics (CFD) – an axial and a radial fan. With local model networks metamodels were built approximating the CFD simulation results. Input selection could automatically discover the inputs considered most relevant by experts. They were selected as the most important ones by the subset selection procedure. Finally, a dynamic process HVAC process has been modeled as a basis for predictive control design. Here the input selection automatically discovered not only the relevant physical inputs but also the orders and dead times. This corresponds to an extremely high-dimensional input selection problem since 6 physical inputs/outputs with a time delay between 1 and 10 acted as potential input variables for the premises and consequents of the local model network.
The substantial contribution by Dr. Julian Belz for writing this chapter is gratefully acknowledged and appreciated.
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Nelles, O. (2020). Input Selection Applications. In: Nonlinear System Identification. Springer, Cham. https://doi.org/10.1007/978-3-030-47439-3_27
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