NDEKF Neural Network Applied to Electronically Controlled Fuel Injection System

The electronically controlled fuel injection system in locomotive diesel is a complicated nonlinear system. So we lead the NARMAX (nonlinear auto-regressive moving average with exogenous inputs) neural network into its model. In order to overcome the deficiency that the neural network structure relies on one's own personal experience, we used the pruning based on the Hession matrix to optimize the network structure. NDEKF (node-decoupled extend Kalman filter) which was adopted to train networks converges more quickly than the back-propagation algorithm does and assists in the avoidance of local minimum. The experiments showed that the hybrid neural networks of the nonlinear auto-regressive with exogenous outputs are very close to the actual results, and the inputs can identify object ranks precisely.