Comparison of clustering algorithms in the identification of Takagi-Sugeno models: A hydrological case study

摘要

In this paper different clustering algorithms are used to identify Takagi-Sugeno models in a data-driven manner. All but one of these clustering algorithms are based on the minimization of an objective function; the other one is the subtractive clustering algorithm. To guide the objective function-based clustering algorithms, an algorithm called ClusterFinder is developed in order to determine the optimal number of clusters as a compromise between model complexity and model accuracy. The hydrological case study considered concerns the modelling of unsaturated groundwater flow. The Takagi-Sugeno models are identified on the basis of an artificially generated training data set for a specific soil type, and can be incorporated into a fuzzy rule-based groundwater model.