کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
740150 | 894143 | 2010 | 10 صفحه PDF | دانلود رایگان |
This paper describes the application of Hysteretic Recurrent Neural Networks (HRNNs) to the modeling of polycrystalline piezoelectric actuators. Because piezoelectric materials exhibit voltage/strain relationships that are hysteretic and rate-dependent, the HRNN is composed of neurons with activation functions that incorporate these characteristics. Individual neurons are shown to agree with existing models of ideal single-crystal piezoelectric behavior. The combination of many such neurons into a network allows prediction of the heterogeneous behavior of polycrystalline materials. This model is shown to approximate the strain and polarization of an unloaded commercial stack actuator at multiple loading rates. A comparison is made to a recurrent Radial Basis Function Network model, and the HRNN is demonstrated to more accurately generalize across data sets. The model is further shown to execute on a PC platform at rates over 100 Hz, fast enough to support its application to real-time control.
Journal: Sensors and Actuators A: Physical - Volume 163, Issue 2, October 2010, Pages 516–525