Separated antecedent and consequent learning for Takagi-Sugeno fuzzy systems
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 Bacterial Evolutionary Algorithm (BEA) and the Levenberg-Marquardt (LM) method. For the linear parameters in fuzzy systems appearing in the rule consequents the Least Squares (LS)...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings 2006 IEEE International Conference on Fuzzy Systems|
|01_Botzheim_Separated_antecedent_and_2006.pdf||179.73 kB||Adobe PDF||Request a copy|
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