An evolving fuzzy system (EFS) is a system that permanently adapts itself to changing environmental conditions. This is done by adjusting its structure and parameters on the basis of observed data. Research in this emerging field has so far mainly focused on learning models with a high (predictive) accuracy. Despite its importance, this criterion is not sufficient, since overly complex models that cannot be understood will likely be refused in practical applications. Without any doubt, fuzzy systems do have the potential to offer both, accuracy and transparency, and the goal of this project is to exploit this high potential. More concretely, the goal is to produce concepts, methods, and algorithms for making EFS more user‐friendly. First of all, this will be achieved by developing methods for reducing the complexity of fuzzy models, thereby making them more transparent and possibly amenable to interpretable linguistic representations. Another important user requirement is reliability. In this regard, different types of uncertainty concerning the model itself and its predictions have to be captured and represented; ideally, a model is “self‐aware” in the sense of being able to judge its own reliability. Finally, novel visualization techniques and methods shall be developed that allow a human user to interact with the learning system in a dynamical way. Jointly, these contributions will greatly increase the practical usefulness and applicability of evolving fuzzy systems.
Funding: DFG (2010-2014)
Contact: Eyke Hüllermeier, Ammar Shaker
Cooperation: Department of Knowledge-Based Mathematical System, University of Linz, Austria