动态递归神经网络及其在机敏机构辨识中的应用  

System Identification of Smart Mechanisms Based on Dynamic Recurrent Neural Networks

作　　者：宋轶民[1] 余跃庆[1] 张策[2] 马文贵[3] 

机构地区：[1]北京工业大学,北京100022 [2]天津大学,天津300072 [3]天津纺织工学院,天津300160

出　　处：《机械科学与技术》2001年第4期515-517,共3页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家自然科学基金 (5 9675 0 0 4);中国博士后科学基金资助

摘　　要：介绍了动态递归神经网络的数学模型 ,提出了一种学习因子自适应调节的改进算法。利用修剪法确定拓扑结构 ,提高了神经网络的泛化能力。以实验数据为样本 ,采用复合辨识方法离线设计了动态递归神经网络辨识器 ,获得了机敏机构的非线性动力学模型 ,其精度明显高于传统的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.

关 键 词：机敏机构 振动控制 神经网络 系统辨识 PRNN 

分 类 号：TH113.1[机械工程—机械设计及理论] TP183[自动化与计算机技术—控制理论与控制工程]