A novel clustering method for fuzzy model identification
Meena Tushir, Smriti Srivastava · 2009
Takagi-Sugeno models are an important class of fuzzy rule based oriented models, generally used for prediction and control. Fuzzy clustering is one of effective methods for identification. In this method, we propose to use a fuzzy clustering method (Kernel based fuzzy c-means method) for automatically constructing a multi-input fuzzy model to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, it also shows some examples of modeling, among them a model of a human operator's control action and a qualitative model to explain the trends in the time series data of the price of a stock.