Modeling and OnLine Control of Nonlinear Systems using Neuro-Fuzzy Learning tuned by Metaheuristic Algorithms
Mourad Turki, Sana Bouzaida, Anis Sakly, Faouzi M’Sahli · International Journal of Control and Automation · 2014
This paper proposes a hybrid learning algorithm for tuning an Adaptive Network based Fuzzy Inference System ANFIS. The proposed scheme of adapting parameters in ANFIS employs evolutionary techniques PSO and GA to adjust the antecedent parameters. The leastsquares (LSE) algorithm is used to adjust consequent parameters. The number of fuzzy rules is fixed and given by using the Xie Beni’s index. This new approach is applied to identify and control nonlinear systems with an on-line strategy. The obtained results are compared to similar ANFIS using gradient descent method GD as antecedent parameters of training algorithm and other methods applied in the same problems.