Robustification of Self-Optimising Systems via Explicit Treatment of Uncertain Information
- 10.2991/ifsa-eusflat-15.2015.25How to use a DOI?
- Self-Optimising Systems, Uncertain Information, Uncertainty Treatment, Regression
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.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jan H. Schoenke AU - Werner Brockmann PY - 2015/06 DA - 2015/06 TI - Robustification of Self-Optimising Systems via Explicit Treatment of Uncertain Information BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 152 EP - 161 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.25 DO - 10.2991/ifsa-eusflat-15.2015.25 ID - Schoenke2015/06 ER -