Towards Robust Evolving Fuzzy Systems

In this chapter, methodologies for more robustness and transparency in evolving fuzzy systems will be demonstrated. After outlining the evolving fuzzy modelling approaches FLEXFIS (=FLEXible Fuzzy Inference Systems) for fuzzy regression models and FLEXFIS-Class (=FLEXible Fuzzy Inference Systems for Classification) for fuzzy classification models, robustness improvement strategies during the incremental learning phase will be explained, including regularization issues for overcoming instabilities in the parameter estimation process, overcoming the unlearning effect, dealing with drifts in data streams, ensuring a better extrapolation behavior and adaptive local error bars serving as local confidence regions surrounding the evolved fuzzy models. The chapter will be concluded with evaluation results on high-dimensional real-world data sets, where the impact of the new methodologies will be presented.