کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
391188 661355 2006 21 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Comparison of clustering algorithms in the identification of Takagi–Sugeno models: A hydrological case study
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Comparison of clustering algorithms in the identification of Takagi–Sugeno models: A hydrological case study
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Fuzzy Sets and Systems - Volume 157, Issue 21, 1 November 2006, Pages 2876-2896