Synchronization of chaotic systems and identification of nonlinear systems by using recurrent hierarchical type-2 fuzzy neural networks.
This paper proposes a novel approach for training of proposed recurrent hierarchical interval type-2 fuzzy neural networks (RHT2FNN) based on the square-root cubature Kalman filters (SCKF). The SCKF algorithm is used to adjust the premise part of the type-2 FNN and the weights of defuzzification and the feedback weights. The recurrence property in the proposed network is the output feeding of each membership function to itself. The proposed RHT2FNN is employed in the sliding mode control scheme for the synchronization of chaotic systems. Unknown functions in the sliding mode control approach are estimated by RHT2FNN. Another application of the proposed RHT2FNN is the identification of dynamic nonlinear systems. The effectiveness of the proposed network and its learning algorithm is verified by several simulation examples. Furthermore, the universal approximation of RHT2FNNs is also shown.
Control Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran. Electronic address: a.mohammadzadeh@tabrizu.ac.ir.
Ardashir
A
Mohammadzadeh
Control Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran. Electronic address: ghaemi@tabrizu.ac.ir.
Sehraneh
S
Ghaemi
D000465:Algorithms; D001185:Artificial Intelligence; D001256:Astronomy; D003198:Computer Simulation; D016208:Databases, Factual; D017143:Fuzzy Logic; D000069550:Machine Learning; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results
Hierarchical fuzzy systems; Recurrent fuzzy systems; Square-root cubature Kalman filter; Type-2 fuzzy neural networks