Compatible cluster merging for fuzzy modelling

Making a fuzzy model of a dynamic process requires the tuning of many parameters. Doing this heuristically is tedious and time consuming. Clustering techniques provide an easier way for forming fuzzy model using measurements made on the system. However, the number of clusters and hence the number of rules the fuzzy rule-base must be determined a priori. It is usually not possible to determine beforehand the optimal number of rules in a rule-base. In this paper, a compatible cluster merging algorithm is suggested for finding the "optimal" number of rules in a rule base. It is based on the compatible cluster merging algorithm proposed recently. The original compatible cluster merging algorithm has certain undesired properties for fuzzy modelling. Hence, a modification is proposed and a modified compatible cluster merging algorithm is described. The new algorithm combines techniques from the original compatible cluster merging, fuzzy multicriteria decision making and heuristics. Examples are given that show the applicability of the proposed method.