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机译:具有分层SEQ2SEQ LSTM的MFR脉冲序列的工作模式识别与边界识别
Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China|Peng Cheng Lab Shenzhen 518055 Peoples R China;
Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China|Peng Cheng Lab Shenzhen 518055 Peoples R China;
Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China;
Peng Cheng Lab Shenzhen 518055 Peoples R China|Northern Inst Elect Equipment Beijing 100091 Peoples R China;
learning (artificial intelligence); radar signal processing; time series; neural nets; feature extraction; multifunction radar work mode; input pulse sequence; received radar pulses stream; multiple work mode class segments; intra-mode; inter-mode knowledge; modern MFR; traditional hand-crafted features; enclosed work mode; transition boundaries; adjacent modes; automatic recognition; MFR work mode sequences; pulse-level; sequence-to-sequence; multiple complexes modulated work mode classes; state-of-the-artwork mode classification methods; work modes recognition; boundary identification; MFR pulse sequences; hierarchical seq2seq LSTM;
机译:使用残差网络和LSTM突破视听单词识别的界限
机译:使用CNN和LSTM堆叠在SEQ2SEQ架构中的MICRNA序列预测
机译:基于使用分层深层LSTM网络的可穿戴传感器的人类活动识别
机译:基于从帧内DNN和LSTM-RNN中提取的后图序列的生成建模的语言识别
机译:大脑如何工作:用于学习和识别的分层和时间模型。
机译:人工神经网络序列到序列LSTM以及外源变量作为否的分析工具
机译:CLSTM:基于深度特征的语音情感识别,使用分层Convlstm网络