Open AccessAn Improved Deep Belief Network for Chinese Emergency Recognition
Author(s) -
Haoran Yin,
Jinxuan Cao,
Luzhe Cao,
Guodong Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1549/2/022065
Subject(s) - softmax function , word2vec , computer science , word (group theory) , deep belief network , artificial intelligence , layer (electronics) , pattern recognition (psychology) , function (biology) , convolution (computer science) , deep learning , artificial neural network , mathematics , chemistry , geometry , organic chemistry , embedding , evolutionary biology , biology
Aiming at the defects that the RBM module in DBN can only re-represent information but cannot extract information features, and can only handle one-dimensional data, the DBN network is improved, and a Conv-DBN model is proposed to recognize emergencies. First, the text corpus is preprocessed, and the word vector matrix generated by Word2Vec is used as input, and then the word vector features are extracted through the visible layer integrated into the convolution operation. Word vector features are used as the input of the next layer. Finally, every layers are fine-tuned through back-propagation at the top layer. The softmax function is used to activate, and the recognition result is output. Simulation results show that the method proposed in this paper has improved accuracy and recall, and F value is better than other methods.