Robustification of Self-Optimising Systems via Explicit Treatment of Uncertain Information
Jan Schoenke, Werner Brockmann · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2015
Uncertainty treatment in self-optimising systems touches two design-issues.Firstly, a valid estimation of uncertainties within the system is impossible beforehand as the uncertainties as well as the systems behaviour changes during run-time due to self-optimisation.Secondly, the design of a selfoptimising system needs to mediate between the often conflicting goals of optimality and robustness.Here we present the concept for a lightweight algorithmic add-on for self-optimising function approximators that enables to reflect uncertainties related to the current state and to flexibly combine optimality and robustness in one design.Illustrating examples of TS-fuzzy systems highlight the properties of our approach.