Research of Optimizing Ignition Control System in Gaseous Fuel Engine Based on RBF Neural Network

Ignition timing is crucial to the performance, efficiency and emissions of a spark ignition engine. This paper presents a new approach to achieve the optimizing ignition control in gaseous fuel engine by using RBF neural network. In order to meet the control objective, the relationship of ignition angle and gaseous fuel engine performance is studied. The learning algorithm of RBF neural network is also described in this paper. The experimental ignition angle and the training result of RBF neural network are compared under various work conditions. Results show that ignition control system can successfully fulfill requirements of gaseous fuel engine. Because of the implementation of optimum ignition system based on RBF neural network, gaseous fuel engine performance is greatly improved. The testing results show that ignition control system established in this paper is accurate and practical.