Abstract
Aiming at the poor performance of traditional data fusion algorithms for high-dimension, high-noise and multi-scale temporal data fusion, a data fusion model based on deep neural network (DDB model) is proposed in this paper. DAE is used to denoise and reconstruct the input data. Secondly, deep convolutional neural network (DCNN) is used to extract spatial and short-term features of the data. The bidirectional long and short-term memory network (BLSTM) is introduced to further extract the long-term time features of the data. Finally, a fully connected network is used to fuse the temporal features of different scales. Experimental results on GE data set show that the fusion performance of this model is superior to BPNN, DCNN and CNN-LSTM data fusion models.
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