内 燃 机 学 报 第 卷（ ）第 期 （ ） condition of 1 500 r/min rotational speed and 72% load rate，K fold cross- validation and particle swarm optimization were used to optimize hyper parameters. The results show that，the ac- curacy of LSTM neural network model established is %，and the accuracy of detecting pre-ignition before CA 10 is %. Compared with back propagation(BP) neural network and support vector machine(SVM)， LSTM has a less root mean square error(RMSE) and is more advanced in discrimination. By receiver operating characteristic curve，it is proved that LSTM is a better discrimination model with a larger area under curve. Com- pared with the limit-based discrimination method，the accuracy of LSTM is higher. LSTM takes both accuracy and advance into account，and accords with the fundamental goal of judging pre-ignition based on ionic current signal. Keywords：gasoline engine；pre-ignition；ionic current；long short-term memory(LSTM)；neural network
