基于支持向量机与改进高斯过程混合模型的车用电池容量预测方法*
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李雨佳, 欧阳权, 刘灏仪, 祝铭烨, 王志胜
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Vehicle Battery Capacity Prediction Based on Hybrid Model of Support Vector Machine and Improved Gaussian Process
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LI Yujia, OUYANG Quan, LIU Haoyi, ZHU Mingye, WANG Zhisheng
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表3 训练集为30%时不同方法预测结果对比
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数据集 | 模型 | RMSE | MAE | MAPE(%) | 实车1 | 混合模型 | 0.042 6 | 0.032 3 | 0.028 8 | SVM | 1.097 1 | 0.055 8 | 0.768 9 | LSTM | 0.319 8 | 0.251 1 | 0.225 6 | BP | 0.105 6 | 0.085 8 | 0.065 1 | 实车2 | 混合模型 | 0.050 5 | 0.033 0 | 0.085 8 | SVM | 0.370 7 | 0.309 1 | 0.297 8 | LSTM | 0.613 2 | 0.583 5 | 0.559 0 | BP | 0.106 6 | 0.086 0 | 0.071 0 | 实车3 | 混合模型 | 0.018 9 | 0.014 3 | 0.013 9 | SVM | 0.088 1 | 0.074 3 | 0.072 1 | LSTM | 0.098 9 | 0.078 6 | 0.076 0 | BP | 0.161 0 | 0.117 9 | 0.114 3 |
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