In this paper we exploit multi-objective genetic algorithms to identify Takagi-Sugeno (TS) fuzzy systems that show simultaneously high accuracy and low complexity. Using this approach, we approximate the Pareto optimal front by first identifying TS models with different structures (i.e., different number of rules and input variables), and then performing a local optimization of these models using an ANFIS learning approach. The results obtained allow determining a posteriori the optimal TS system for the specific application. Main features of our approach are selection of the input variables and automatic determination of the number of rules.
Identification of Takagi-Sugeno Fuzzy Systems based on Multi-Objective Genetic Algorithms
COCOCCIONI, MARCO;LAZZERINI, BEATRICE;MARCELLONI, FRANCESCO
2005-01-01
Abstract
In this paper we exploit multi-objective genetic algorithms to identify Takagi-Sugeno (TS) fuzzy systems that show simultaneously high accuracy and low complexity. Using this approach, we approximate the Pareto optimal front by first identifying TS models with different structures (i.e., different number of rules and input variables), and then performing a local optimization of these models using an ANFIS learning approach. The results obtained allow determining a posteriori the optimal TS system for the specific application. Main features of our approach are selection of the input variables and automatic determination of the number of rules.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
