MADALINE Neural Network with Truncated Momentum for LTV MIMO System Identification
Presented in this paper is a new version of the Multi- ADAptive LINear Element (MADALINE) neural network for Online System identification of linear time-varying (LTV) Multi- Input Multi-Output (MIMO) systems. A truncated momentum term is used in the learning algorithm for the purpose of reducing fluctuation while sudden parameter change happens thus offers a smoother transition in tracking the parameter. Based on the input output polynomial model, which can be easily transformed into the row canonical state space model, Tapped delay lines are introduced, so the MADALINE becomes recurrent in nature and thus is suitable for parameter estimation of such systems. The MADALINE can then be setup under the assumption that the system structure is known in advance. The estimated parameters are obtained as the weights of trained individual neurons of the MADALINE. The method is implemented in MATLAB and simulation study was then performed on a few well known examples. Simulation results show that the algorithms offer satisfactory performance. This work is based on our previous work on Multi-Input Multi-Output systems’ identification 18.
– System identification MIMO Parameter estimation Neural network MADALINE.
Wenle Zhang
Dept. of Engineering Technology University of Arkansas at Little Rock Little Rock, AR 72204
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
1591-1596
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）