Abstract
Magnetorheological (MR) fluid damper is a semi� active control device that has recently received more attention by the vibration control community. But inherent hysteretic and highly nonlinear dynamics of MR fluid damper is one of the challenging aspects to employ its unique characteristics. The combination of artificial neural network (ANN) and fuzzy logic system (FLS) have been used to imitate more precisely the behavior of this device. However, the derivativebased nature of adaptive ne tworks causes some deficiencies. Therefore, in this paper, a novel approach that employ genetic algorithm, as a freederivative algo rithm, to enhance the capability of fuzzy systems, is proposed. The proposed method used to model MR damper. The results will be compared with adaptive neurofuzzy inference system (ANFIS) model , which is one of the wellknown approaches in soft computing fram ework, and two best parametric models of MR damper. Data are generated based on benchmark program by applying a number of famous earthquake records.
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