摘要
利用多层前馈神经网络的非线性建模特性,基于动态BP网络的串并联和并联模型,提出一种高鲁棒性BP算法.与传统的BP算法相比,鲁棒BP算法有5个优点:(1)适合于非线性动态系统辨识;(2)辨识精度高;(3)不必内插所有训练样本;(4)具有高鲁棒性,能抵制过失误差和量测误差;(5)收敛速度得到了改进,因为错误样本的影响得到了适度的抑制.把该算法用于非线性动态系统辨识。
The paper presents a high robust BP algorithm using the nonlinear of multilayer feedforward neural networks and based on the series parallel model of the dynamic BP network. In contrast to the conventional BP algorithm,five advantages of the robust BP algorithm are:(1) fitting to the dynamic identification of nonlinear system;(2) the identifical accuracy is very high;(3) not interpolating all the training points;(4) it is robust against gross errors and measuring errors;(5) its rate of convergence is improved since the influence of incorrect sample is gracefully suppressed.The algorithm is applied to the dynamic identification of nonlinear system and the simulation result shows the new method is efficient.
出处
《信息与控制》
CSCD
北大核心
1996年第6期354-360,共7页
Information and Control
关键词
非线性动态系统
BP算法
系统辨识
nonlinear system, dynamic identification, robust BP algorithm, maximum likelihood method