A hybrid nonlinear autoregressive neural network for permanent-magnet linear synchronous motor identification
Gang Lv, Fan Yu · 2005
The modeling of permanent-magnet linear synchronous motor is very important to the control and the static and dynamic characters analysis of the system. In this paper, the model of permanent-magnet linear synchronous motor is presented by using neural networks of the nonlinear autoregressive with exogenous inputs. Based on the same cost function, residual signal analysis is mixed into the networks, and then the networks can identify motor's ranks automatically. 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. 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 identify 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.