Skip to Main Content
This paper proposes a new approach to system modeling using continuous-time recurrent fuzzy systems (CTRFS). The approach is based on the representation of CTRFS as hybrid systems. With this representation, various forms of a priori knowledge about the system to be modeled can be incorporated. This allows a reasonable reduction of optimization parameters and hence, avoids overfitting. Furthermore, the presented approach offers a deeper insight into the system structure on the basis of measurement data solely. This is illustrated by a selection algorithm for relevant input and state variables. The applicability of the approach is shown by modeling a chemical process.