Permanent-magnet Linear Synchronous Motor Model Using NDEKF Neural Network on Hession Optimization

The nonlinear autoregressive with exogenous inputs model was expanded into the polynomial function,then the condition which true ranked satisfy was presented by using residual signal analysis.In order to overcome the shortages that the design of network structure was depended on one's own personal experience,Hession-based network pruning was used to get the optimization network structures.Some shortages of BP(back-propagation algorithm)were considered,so NDEKF(node-decoupled extend Kalman filter)was applied to train networks.The experiment results showed that the hybrid neural networks of the nonlinear autoregressive with exogenous inputs can identified object's rank precisely,and the output of networks was very close to the experimental result.In the experiments,the performance of NDEKF was 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.