Abstract:To solve the blank of current research on the prediction of density limit disruption of EAST, 972 density limit disruptive pulses selected as data sets from the EAST’s 2014 to 2019 discharge. 13 diagnostic signals were chosen as features. Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) was used as models and the disruption risk was used as output to build the predictors. The experimental results show that for density limit disruptive pulses, under different alarming times, the successful prediction rate of LSTM (around 95%) is higher than that of MLP (85%), and for non-disruptive pulses, the false prediction rate is around 8% for both MLP and LSTM. The performance of LSTM has great improvement than MLP, shows the feasibility of building EAST density limit disruption system with neural networks and improving the response performance of disruption avoidance and mitigation system.