ARFNNs under Different Types SVR for Identification of Nonlinear Magneto-Rheological Damper Systems with Outliers
نویسندگان
چکیده
This paper demonstrates different types support vector regression (SVR) for annealing robust fuzzy neural networks (ARFNNs) to identification of nonlinear magneto-rheological (MR) damper with outliers. A SVR has the good performances to determine the number of rule in the simplified fuzzy inference system and initial weights for the fuzzy neural networks. In this paper, we independently proposed two different types SVR for the ARFNNs. Hence, a combination model that fuses simplified fuzzy inference system, SVR and radial basis function networks is used. Based on these initial structures, and then annealing robust learning algorithm (ARLA) can be used effectively to adjust the parameters of structures. Simulation results show the superiority of the proposed method with the different types SVR for the nonlinear MR damper systems with outliers.
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