关键词: 深度学习, 智能制造, Encoder-Decoder框架, 问答系统, 长短记忆神经网络, 门机制, 相似度

Abstract: To construct the intelligent manufacturing Question-Answering (QA) system,facilitate the knowledge sharing of intelligent manufacturing and accelerate the intelligent manufacturing industrial layout,the deep learning algorithm was used to improve the problems that the construction process of the traditional QA System was too complicated,the requirement of human intervention and prior knowledge was too high,the questions and answers could not be effectively mapped and so on.Long Short-Term Memory (LSTM) deep neural network algorithm was applied to avoid the gradient disappearance and gradient explosion problems of the general deep learning algorithm.The gate mechanism in LSTM algorithm could avoid the influence of chain rule on gradient over-optimization and analyze the semantics of sentence directly.The similarity calculation was used to determine the correct answer or not.The verification experiments on the validation set showed that the proposed method of semantic analysis had a clear improvement in the accuracy of QA System.

Key words: deep learning, intelligent manufacturing, Encoder-Decoder frame, question-answering system, long short-term memory, gate mechanism, similarity