Process production process quality prediction algorithm fused with Mar-G LSTM
Aiming at the characteristics of process production with strong continuity and complex temporal coupling,and the problem that traditional neural networks do not have long-term memory capability and are prone to training parameter disas-ters and gradient explosion during deep network training,a combined prediction model based on incorporates Gated Recur-rent Units(GRU)of Markov optimization and Long and Short-Term Memory(LSTM)networks named Mar-G LSTM was proposed.A deep LSTM neural network model was constructed by incorporating the gating mechanism into the recurrent neural network structure to selectively memorise the process production timing data information and learn the information dependence of timing data sequences,thus solving the gradient explosion problem during training.At the same time,the prediction results of the GRU-LSTM model were modified and optimised by combining Markov chain,which further im-proved the prediction accuracy while reducing the complexity of the model.The prediction accuracy of the model was further improved.The results showed that the Mar-G LSTM algorithm improved the prediction accuracy by 37.42％,21.32％,17.91％and 12.56％compared with the random forest model,the GRU model,the LSTM model and the combined Conv-olutional Neural Network and GRU network(CNN-GRU)model respectively.The proposed Mar-G LSTM algorithm could achieve accurate prediction of process production quality,which provided an idea and a way to reduce the completion time of process parameter regulation tasks.
process productionprocess quality predictiongate recurrent unitlong short-term memoryMarkov chains
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