A Study on Uncertainty–Complexity Tradeoffs for Dynamic Nonlinear Sensor Compensation

In this paper, we focus on the design of reduced-complexity sensor compensation modules based on learning-from-examples techniques. A multiobjective optimization design framework is proposed, where system complexity and compensation uncertainty are considered as two conflicting costs to be jointly minimized. In addition, suitable statistical techniques are applied to cope with the variability in the uncertainty estimation arising from the limited availability of data at design time. Numerical simulations are provided on a set of synthetic models to show the validity of the proposed methodology.

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