会议专题

Identification of Nonlinear Dynamic Systems Using Modified DRNNs

In order to apply the better diagonal recurrent neural network (DRNN) to nonlinear dynamic system identification, three different modified DRNNs are proposed and compared. DRNN has more dynamic mapping capability than feedforward neural network (FNN). Meanwhile, it has simpler structure and needs less training time than full recurrent neural network (FRNN). To overcome the insufficiency of the time variable character of the weight vectors in previous research, this work modifies the training algorithm and applies to the three DRNN models including DRNN, higher order DRNN (HDRNN) and quasi DRNN (QDRNN). Meanwhile, both MIMO and SISO nonlinear dynamic systems are tested. The simulations show that the three models using modified algorithm have better performance than original models. Modified QDRNN has the best performance.

system identification nonlinear dynamic system DRNN training algorithm

Mu Yuqiang SHENG Andong QIAN Longjun

Department of Automation, Nanjing University of Science and Technology, Nanjing, 210094

国际会议

第七届国际测试技术研讨会

北京

英文

2007-08-05(万方平台首次上网日期,不代表论文的发表时间)