Neuro fuzzy model for predicting the dynamic characteristics of beams

Optimal neuro‐fuzzy control of hepatitis C virus integrated by genetic algorithm

“…To clarify the employed ANFIS structure, let us consider a Takagi–Sugeno fuzzy inference system with two inputs and one output. For this system, the ‘if–then’ rules are presented as [20–22] where A 1 , B 1 , A 2 , and B 2 are membership functions considered to make the ANFIS structure as shown in Fig. 1 [20].…”

“…The contribution of each rule in the model output is obtained in layer 4 based on Takagi–Sugeno rules ( as presented in (7) and (8)). Finally, in layer 5, the weighted global output of the system is calculated using (9) [20].…”

Incorporating feedforward neural network within finite element analysis for L-bending springback prediction

“…Therefore, there were many attempts by researchers to combine the FE and ANN in order to take advantage of the capabilities of both approaches, which would reduce laboratory experimental cost and increase prediction accuracy (Apalak, Ekici, Yildirim, & Apalak, 2014;Bachi, Abdulrazzaq, & He, 2014;Bheemreddy, Chandrashekhara, Dharani, & Hilmas, 2013;Hasançebi & Dumlupınar, 2013;Khalaj Khalajestani & Bahaari;Selvakumar, Arulshri, Padmanaban, & Sasikumar, 2013;Tian, Luo, Wang, & Wu, 2014). From the beginning of its usage until recently, most studies related to the combination of ANN and FE methods in solving engineering problems have used inverse analysis aimed at optimising the FE simulation parameters.…”