Title :
A novel training method based on variable structure systems approach for interval type-2 fuzzy neural networks
Author :
Kayacan, Erdal ; Cigdem, Ozkan ; Kaynak, Okyay
Author_Institution :
Dept. of Electr. & Electron. Eng., Bogazici Univ., Istanbul, Turkey
Abstract :
Type-2 fuzzy logic systems have been applied in various control problems because of their abilities to model uncertainties in a more effective way than type-1 fuzzy logic systems. In this paper, a novel learning algorithm is proposed to train type-2 fuzzy neural networks. In the approach, instead of trying to minimize an error function, the weights of the network are tuned by the proposed algorithm in a way that the error is enforced to satisfy a stable equation. The parameter update rules are derived and the convergence of the weights is proved by Lyapunov stability method. To illustrate the applicability and the efficacy of the proposed method, the control problem of Duffing oscillator with uncertainties and disturbances is studied. The simulation studies indicate that the type-2 fuzzy structure with the proposed learning algorithm result in a better performance than its type-1 fuzzy counterpart.
Keywords :
Lyapunov methods; fuzzy control; fuzzy neural nets; learning (artificial intelligence); neurocontrollers; oscillators; uncertain systems; variable structure systems; Duffing oscillator; Lyapunov stability; control problem; disturbance; interval type-2 fuzzy neural network; learning algorithm; parameter update rule; training method; uncertainty; variable structure system; weight convergence; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Heuristic algorithms; Oscillators; PD control; Uncertainty; Duffing oscillator; Feedback-error-learning; Type-2 fuzzy neural networks; Variable structure systems;
Conference_Titel :
Advances in Type-2 Fuzzy Logic Systems (T2FUZZ), 2011 IEEE Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-61284-077-2
DOI :
10.1109/T2FUZZ.2011.5949557