Nonlinear System Identification Using Type-2 Fuzzy Recurrent Wavelet Neural Network
کد مقاله : 1057-CFIS (R1)
نویسندگان:
بی بی الهام فلاح تفتی1، محمد تشنه لب *2، مجتبی احمدیه خانه سر3
1کامپیوتر، مکانیک برق و کامپیوتر،آزاد اسلامی واحد علوم و تحقیقات تهران، تهران، ایران
2دانشگاه خواجه نصیرالدین طوسی
3Faculty of Engineering University of Nottingham, Nottingham, NG7 2RD, U.K
چکیده مقاله:
In this paper, the integration of Type-2 fuzzy set theory and recurrent wavelet neural network(WNN) is proposed to allow managing of non-uniform uncertainties for identifying non-linear dynamic system. The proposed Type-2 fuzzy WNN is inherently a recurrent multilayered network which constructed based on a set of Type-2 fuzzy rules and recurrent connections in the second layer of the FWNN. Each rule comprises a wavelet function in the consequent part. The structure has both advantages of recurrent and wavelet neural network which expand the basic ability of fuzzy neural network to deal with temporal problems. Both antecedent and consequent parameters update rules are derived based on the gradient descent method. The structure is applied in the identification of dynamic plants which is commonly used in the literature. Simulation result from the identification of a second-order non-linear plant confirms the better performance and effectiveness of the proposed structure.
کلیدواژه ها:
Identification, Recurrent neural network, Wavelet Neural Network, Type-2 fuzzy logic, Gradient Descent
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