NDEKF Neural Network Applied to Electronically Controlled Fuel Injection System

摘要：

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 ones 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 objects ranks precisely.

关键词： neural networks diesel engine electronically controlled fuel injection NDEKF Hession optimization

作者: LIU Biao WANG Lide SHEN Ping LV Gang

作者单位: Beijing Jiaotong University, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）