Multi objective optimization of ANFIS structure
Vahid Seydi, Mahdi Aliyari Shoorehdeli, Arash Sharifi, Mohammad Teshnehlab · 2007
This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS).This approach based on multi objective optimization mechanism for training parameters in antecedent part. It considers two cost functions as the objectives which are the maximum difference measurements between the real nonlinear system and the nonlinear model, and training mean square error (MSE). The NSGA-II is the multi objective optimization algorithm which employed for this purpose. So we use gradient decent (GD) method for training all parameters in conclusion part. Finally we show simulation results of applied this method to some nonlinear identification system.