An algorithmic methodology for identifying and modelling nonlinear control strategies is proposed. The methodology presented is based on choices of different fuzzy clustering algorithms, projection of clusters and merging techniques. The best features of well-known clustering methods such as the Gustafson-Kessel and mountain method are also combined. The projection of the prototypes and variables of clusters is a recognized approach to extracting the information included in the data clusters into fuzzy sets. Merging these fuzzy sets, based on the proposed guidelines described, can minimize the number of rules and make the identifying control strategy more transparent. Some improvements to the resulting fuzzy system can be achieved by using optimization methods such as the gradient method. The proposed methodology is based on making the right choice of the right tools and can be described as a universal approximation in terms of identifying and modelling nonlinear control strategies. The control strategy of an underwater vehicle for avoiding objects is identified using this methodology. Results are discussed as well as some conclusions about the proposed method.
Fuzzy clustering methods for identifying and modelling of nonlinear control strategies
2001
16 Seiten, 22 Quellen
Aufsatz (Zeitschrift)
Englisch
Fuzzy Clustering Methods as Techniques for Identifying Control Strategies for an Underwater Vehicle
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