{"id":{"repo_id":"aachen","oai_identifier":"oai:publications.rwth-aachen.de:59797"},"canonical_url":"https://search.dev.ndltd.org/etd/aachen/oai:publications.rwth-aachen.de:59797","repository":{"repo_id":"aachen","name":"RWTH Aachen University","base_url":"https://publications.rwth-aachen.de/oai2d"},"display":{"title":"Dynamic fuzzy pattern recognition","abstract":"Dynamic pattern recognition is concerned with the recognition of clusters of dynamic objects, i.e. recognition of typical states in the dynamic behaviour of a system under consideration. The goal of this thesis is to investigate this new field of dynamic pattern recognition and to develop new methods for clustering and classification in a dynamic environment. The first topic of investigation is the procedure of dynamic classifier design enabling a design of an adaptive classifier that can automatically recognise new cluster structures as time passes. The algorithm proposed in this thesis is introduced in the framework of unsupervised learning and takes advantage of fuzzy set theory which provides a unique mechanism for gradual assignment of objects to clusters and allows to detect a gradual temporal transition of objects between clusters. Another topic addressed in this book is the definition of similarity measures for trajectories of dynamic objects. A number of similarity measures for trajectories are proposed which take into consideration either the pointwise closeness of trajectories in the feature space or the best match of trajectories with respect to their shape. In order to demonstrate the practical relevance of the algorithm developed here, it was applied to two different problems. The first economic problem is concerned with bank customer segmentation based on the analysis of the customers' behaviour. The second technical problem is related to the recognition of typical states in computer network load. 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