Oliver Nelles

Learn More
This paper presents a new, supervised, hierarchical clustering algorithm (SUHICLUST) for fuzzy model identification. The presented algorithm solves the problem of global model accuracy together with the interpretability of local models as valid linearizations of the modeled nonlinear system. The algorithm combines the merits of supervised, hierarchical(More)
  • O. Nelles
  • 2006 IEEE Conference on Computer Aided Control…
  • 2006
Local model networks, also known as Takagi-Sugeno neuro-fuzzy systems, have become an increasingly popular nonlinear model architecture. Usually the local models are linearly parameterized and those parameters are typically estimated by some least squares approach. However, widely different strategies have been pursued for the partitioning of the input(More)
This paper deals with the problem of fuzzy nonlinear model identification in the framework of a local model network (LMN). A new iterative identification approach is proposed, where supervised and unsupervised learning are combined to optimize the structure of the LMN. For the purpose of fitting the cluster-centers to the process nonlinearity, the(More)
In this paper the new algorithm SUHICLUST (SUpervised HIerarchical CLUSTering) is presented. It unifies the strengths of the supervised, incremental construction scheme LOLIMOT with the advantages of product space clustering. The result of this fusion is a powerful structure identification algorithm that enables approximation of processes with axes-oblique(More)