Separated Antecedent and Consequent Learning of Takagi-Sugeno Fuzzy Models

In this paper a new algorithm for the learning of Takagi-Sugeno fuzzy systems is introduced. In the algorithm different learning techniques are applied for the antecedent and the consequent parameters of the fuzzy system. We propose a hybrid method for the antecedent parameters learning based on the combination of the {\em Bacterial Evolutionary Algorithm (BEA)} and the {\em Levenberg-Marquardt (LM)} method. For the linear parameters in fuzzy systems appearing in the rule consequents the {\em Least Squares (LS)} and the {\em Recursive Least Squares (RLS)} techniques are applied, which will lead to a global optimal solution of linear parameter vectors in the least squares sense. Therefore a better performance can be guaranteed than with a complete learning by BEA and LM. The paper is concluded by evaluation results based on high-dimensional test data. These evaluation results compare the new method with some conventional fuzzy training methods with respect to approximation accuracy and model complexity.