An Extended ADALINE Neural Network Trained by Levenberg- Marquardt Method for System Identification of Linear Systems

摘要：

　　This paper presents a sliding-window version of online identification method for linear time varying systems based on the ADaptive LINear Element – ADALINE (Widrow and Lehr,1990) neural network trained with Levenberg- Marquardt method which offers faster tracking of system parameter change.It is well known ADALINE is slow in convergence which is not appropriate for online application and identification of time varying system.To speed up convergence of learning and thus increase the capability of tracking time varying system parameters,our previous work added a momentum term to the weight adjustment.While the momentum does speed up convergence,it also shows overshooting or oscillating and also tracks noise closely.The Levenberg-Marquardt method is explored in this paper.Simulation results show that the proposed method provides indeed fast yet smoother convergence and better tracking of time varying parameters.

关键词： System identification feedback neural network ADALINE Levenberg-Marquardt

作者: Wenle Zhang

作者单位: Dept. of Engineering TechnologyUniversity of Arkansas at Little RockLittle Rock, AR 72204

会议类型: 国际会议

会议名称: the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)

会议地点: 贵阳

会议语种:英文

页码: 2453-2458

在线出版日期: 2013-05-01（万方平台首次上网日期，不代表论文的发表时间）