├── 2018会议材料 ├── 1611.pdf ├── 2018CCDCCall_for_Papers-cn.pdf ├── CCDC_en (1).doc ├── CCDC_en.doc └── CCDC_en.rar ├── NASA锂电池数据 ├── B0005.mat ├── B0006.mat ├── B0007.mat ├── B0018.mat ├── Havedata.m ├── README.txt ├── 基于数据驱动的卫星锂离子电池寿命预测方法_艾力.pdf ├── 实验结果.docx └── 锂电池数据说明.doc ├── VISIO文件 ├── HETU.vsdx ├── LSTM 英文版.vsdx ├── LSTM.vsdx ├── LSTMstruction.vsd ├── RNN.vsdx ├── improvedLSTM.vsdx ├── improvedLSTM英文版.vsdx ├── 图片1.png ├── 图片2.png ├── 学习率1.vsdx └── 绘图1.vsdx ├── 公式 └── heji.wmf ├── 参考文献 ├── Prognostics-for-state-of-health-estimation-of-lithium-ion-_2013_Microelectro.pdf ├── Robust-prognostics-for-state-of-health-estimation-of-lit_2015_Microelectroni.pdf ├── Sampling-based-State-of-Health-estimation-methodology_2015_Journal-of-Power-.pdf ├── 基于LSTM循环神经网络的故障时间序列预测_王鑫.pdf ├── 基于数据驱动的卫星锂离子电池寿命预测方法_艾力.pdf ├── 基于深度学习的多模态故障诊断方法.docx └── 锂离子电池寿命预测国外研究现状综述_罗伟林.pdf ├── 程序 ├── AR.m ├── DATA.mat ├── Data3.m ├── ErrorCost1.mat ├── ErrorCost2.mat ├── ErrorCost3.mat ├── LSTM_data_process.m ├── LSTM_updata_weight.m ├── Producedata.m ├── ReadMe.txt ├── RunLstm.m ├── Unicode.txt ├── bp158.mat ├── data.txt ├── data2.mat ├── lstm_result.mat ├── para.mat ├── untitled2.fig ├── 实验结果 │ ├── untitled1.fig │ └── untitled2.fig └── 画柱状图程序 │ └── Untitled.m └── 论文 ├── IEEE_Copyright ├── IEEE_Copyright.pdf └── 示例.pdf ├── PDF审查后的论文 ├── PID5261609.pdf └── 认证收条.txt ├── 修改意见后 └── RUL Prognostics Method Based on Real Time Updating of LSTM Parameters.doc ├── 初稿修改论文 ├── 18ccdc英文第三版-zhou.doc ├── 中文第一版.docx ├── 中文第二版.docx ├── 英文第一版.docx ├── 英文第三版.doc └── 英文第二版.doc ├── 初稿提交论文 ├── RUL Prognostics Method Based on Real Time Updating of LSTM Parameters.doc └── RUL Prognostics Method Based on Real Time Updating of LSTM Parameters.pdf ├── 审稿人意见 ├── AE_Comment.txt ├── Review2182.txt ├── Review2185.txt ├── Review2186.txt └── 中文整体翻译.txt ├── 汇款回执 ├── TIM图片20180315165140.jpg └── 汇款回执，论文1611.jpg ├── 终稿提交三份材料 ├── IEEE版权表.pdf ├── PID5261609.pdf └── 认证收条.txt └── 终稿提交论文 ├── RUL Prognostics Method Based on Real Time 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