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In this paper an idea for classification of energy consumption profiles using an evolving c-regression method is presented. Cluster prototypes (centers) are usually defined as a mean of data around the center. The cluster center is a vector of numerical values. The method presented in this paper uses Takagi-Sugeno fuzzy models as a cluster prototype. Beside the method description also preliminary...
In this paper a new approach to data stream evolving fuzzy model identification is given. The structure of the model is given in the form of Takagi-Sugeno and the partitioning of the input-output space is obtained using a fuzzy c-regression clustering method and the approach also involves the evolving properties. The method is given in a recursive form. The proposed approach is shown with two simple...
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 Gustafsson–Kessel (GK) fuzzy clustering, i.e., unsupervised...
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