This paper presents the results of modeling a magnetorheological (MR) damper by means of a neural network. TheMR damper is a device extensively used to mitigate hazardous vibrations in systems such as vehicles, bridges and buildings. The advantages of these dampers over other devices of their class rely on the cost, size, simplicity and performance. However, these devices are highly nonlinear; their dynamics is characterized by friction and hysteresis, phenomena that has been difficult to model following physical laws. In this paper, a recurrent neural network is trained to reproduce the behavior of the damper and it will be shown that the results are better that those obtained by approximate physical models.
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