System identification of smart mechanisms based on dynamic recurrent neural networks

被引：0

作者：

Song, Y.-M. ^{[1
]}

Yu, Y.-Q. ^{[1
]}

Zhang, C. ^{[1
]}

Ma, W.-G. ^{[1
]}

机构：

[1] Beijing Polytechnic University, Beijing 100022, China

来源：

Jixie Kexue Yu Jishu/Mechanical Science and Technology | 2001年 / 20卷 / 04期

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摘要：

In this paper, the dynamic recurrent neural network (DRNN) was applied to the estimation of a smart mechanism featuring piezoceramic actuators and strain gage sensors. The mathematical model of a 4-layered DRNN was presented firstly. To guarantee convergence, a fast learning algorithm VLR was proposed for the DRNN whose learning rate could be regulated adaptively in accordance with Equation (28). Moreover, the suitable topology of the DRNN was determined utilizing the pruning algorithm so as to increase its generalization capability. On the basis of these improvements, a DRNN identifier was designed off-line by means of the compound identification method shown in Figure 3. As can be seen from Figures 4 and 5, the identifier obtained is proved to be more accurate than the KED theoretical model and may be used for suppressing the elastodynamic responses of flexible linkage mechanisms with resort to the neural networks based model reference adaptive control (MRAC) strategy.

引用

页码：515 / 517