Oliver Bänfer

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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)
The idea of a learning strategy extension for nonlinear system identification with local polynomial model networks is presented in this paper. Usually the polynomial model tree (POLYMOT) algorithm utilizes a one-step-ahead optimal learning strategy. A demonstration example shows that this greedy behavior is not the best choice to reach a satisfying global(More)
A comparison of three different subset selection methods in combination with a new learning algorithm for nonlinear system identification with local models of higher polynomial degree is presented in this paper. Usually the local models are linearly parameterized and those parameters are typically estimated by some least squares approach. For the(More)
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