Permanent-Magnet Linear Synchronous Motor Model Using NDEKF Neural Network on Hession Optimization
Fan Yu · Journal of Transportation Systems Engineering and Information Technology · 2006
The modeling of permanent-magnet linear synchronous motor is very important to the control and the static and dynamic characters analysis of the system.First,the nonlinear autoregressive with exogenous inputs model is expanded into the polynomial function,then the condition which true ranks satisfy is presented by using residual signal analysis.In order to overcome the shortages that the design of network structure is depended on one's own personal experience,Hession-based network pruning is used to get the optimization network structures.Some shortages of BP(back-propagation algorithm) are considered,so NDEKF((node-decoupled extend Kalman filter)is applied to train networks.The experiment results show that the hybrid neural networks of the nonlinear autoregressive with exogenous inputs can identified object's(a vertical transport system driven by permanent-magnet linear synchronous motor) ranks precisely,and the output of networks is very close to the experimental result.In the experiments,the performance of NDEKF is often superior to that of BP,while requiring significantly fewer presentations of training data than BP and less over training time than that of BP.