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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 compares a genetic programming (GP) approach with a greedy partition algorithm (LOLIMOT) for structure identi®cation of local linear neuro-fuzzy models. The crisp linear conclusion part of a Takagi-Sugeno-Kang (TSK) fuzzy rule describes the underlying model in the local region speci®ed in the premise. The objective of structure identi®cation is(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)
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)
Any data based method is vulnerable to the problem of extrapolation, nonetheless there exists no unified theory on handling it. The main topic of this publication is to point out the differences in definitions of extrapolation and related methods. There are many different interpretations of extrapolation and a multitude of methods and algorithms, which(More)
A new training 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 utilization of higher degree polynomials this procedure is no longer feasible since the(More)