基于改进的BP算法的非线性稳定环辨识  

Identification of Nonlinear Stability Loop Based on Improved BP Algorithm

作　　者：娄奥 姚敏立 袁丁 LOU Ao YAO;Min-li;YUAN Ding(Rocket Force University of Engineering, Xi'an 710025, China)

机构地区：[1]火箭军工程大学

出　　处：《电光与控制》2019年第11期15-18,69,共5页Electronics Optics & Control

基　　金：国家自然科学基金(61179004,61179005)

摘　　要："动中通"伺服系统的稳定环因受齿隙摩擦等因素的影响,表现出较强的非线性特征。标准BP算法对非线性系统虽有较好的辨识效果,但存在网络收敛慢、过程振荡、泛化能力差等缺点。为弥补这些不足,提出了基于累积误差函数梯度的双学习步长的自适应BP算法,以加快收敛、减少振荡,并设置全局误差阈值控制训练次数,进一步提升泛化能力。通过在"动中通"平台上设计实验,验证了改进后算法在收敛性、辨识精度、泛化能力等方面都有明显提升,可以得到非线性稳定环更精确的BP网络模型。The stability loop of the Satcom On-the-Move(SOTM) servo system is strongly nonlinear due to the influence of backlash friction and other factors.Although the standard Back-Propagation(BP) algorithm has good identification effect for nonlinear systems,it has some shortcomings such as slow network convergence,process oscillation and poor generalization ability.To make up for the shortcomings,an improved adaptive BP algorithm based on the cumulative error function gradient and the double learning step is proposed to accelerate convergence and reduce oscillation.The global error threshold is also set to control training times in order to further enhance its generalization ability.Experiments are designed on the SOTM platform,and it is verified that the improved algorithm has obvious improvement in convergence,identification accuracy,the generalization ability and so on.A more accurate BP network model with nonlinear stability loop can be obtained.

关 键 词：系统辨识 非线性稳定环 BP神经网络 自适应步长 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP273[自动化与计算机技术—控制科学与工程]