In this thesis, we presented the field of evolving systems with an emphasis on ones with fuzzy logic. The main focus of this work is a recently presented evolving eGAUSS+ system. The system is based on Gaussian clustering, where clusters are merged depending on the comparison between the sum of volumes of two clusters. The method was modified and a new input parameter for cluster volume control was added. We analysed the input parameters of the system, and evaluated their impact on clustering performance. We showed that the added input parameter significantly improves the repeatability of unsupervised clustering. This method can be used for input-output identification and supervised classification. The performance of input-output identification was evaluated on two different, nonlinear dynamical systems. We determined the confidence interval of model output and examined the effect of filtering of regressors on clustering and identification as well. The method performance was evaluated for supervised classification and the obtained results were compared with other classifiers.
|
