参考文献/References:
[1] Zhang Jingliang,Lee J. A review on prognostics and health monitoring of li-ion battery[J]. Journal of Power Sources,2011,196(15):6007-6014.
[2]Kim J G,Son B,Mukherjee S,et al. A review of lithium and non-lithium based solid state batteries[J]. Journal of Power Sources,2015,282(1):299-322.
[3]Liu Datong,Pang Jingyue,Zhou Jianbao,et al. Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression[J]. Microelectronics Reliability,2013,53(6):832-839.
[4]Biagetti T,Sciubba E. Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems[J]. Energy,2004,29(12-15):2553-2572.
[5]Zheng Xiujuan,Fang Huajing. An integrated unscented Kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction[J]. Reliability Engineering & System Safety,2015,144(6):74-82.
[6]Yu Jianbo. State-of-health monitoring and prediction of lithium-ion battery using probabilistic indication and state-space model[J]. IEEE Transactions on Instrumentation & Measurement,2015,64(11):2937-2949.
[7]Mo Baohua,Yu Jingsong,Tang Diyin,et al. A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter[C]//Proceedings of the IEEE International Conference on Prognostics and Health Management. Ottawa,Canada:IEEE,2016:1-5.
[8]赖少发,刘华军. 机动目标跟踪支持向量回归学习新方法[J]. 南京理工大学学报,2017,41(2):264-268.
Lai Shaofa,Liu Huajun. Novel approach in maneuvering target tracking based on support vector regression[J]. Journal of Nanjing University of Science and Technology,2017,41(2):264-268.
[9]Wang Shuai,Zhao Lingling,Su Xiaohong,et al. Prognostics of lithium-ion batteries based on battery performance analysis and flexible support vector regression[J]. Energies,2014,7(10):6492-6508.
[10]Dong Hancheng,Jin Xiaoning,Lou Yangbing,et al. Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter[J]. Journal of Power Sources,2014,271(11):114-123.
[11]Qin Taichun,Zeng Shengkui,Guo Jianbin. Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO-SVR model[J]. Microelectronics Reliability,2015,55(9-10):1280-1284.
[12]Ng S S Y,Xing Yinjiao,Tsui K L. A naive Bayes model for robust remaining useful life prediction of lithium-ion battery[J]. Applied Energy,2014,118(4):114-123.
[13]王健峰,张磊,陈国兴,等. 基于改进的网格搜索法的SVM参数优化[J]. 应用科技,2012,39(3):28-31.
Wang Jianfeng,Zhang Lei,Chen Guoxing,et al. A parameter optimization method for an SVM based on improved grid search algorithm[J]. Applied Science and Technology,2012,39(3):28-31.
[14]张讲社,郭高. 加权稳健支撑向量回归方法[J]. 计算机学报,2005,28(7):1171-1177.
Zhang Jiangshe,Guo Gao. Reweighted robust support vector regression method[J]. Chinese Journal of Computers,2005,28(7):1171-1177.
[15]田盛丰. 基于核函数的学习算法[J]. 北方交通大学学报,2003,27(2):1-8.
Tian Shengfeng. Kernal-based learning algorithms[J]. Journal of Northern Jiaotong University,2003,27(2):1-8.
[16]边霞,米良. 遗传算法理论及其应用研究进展[J]. 计算机应用研究,2010,27(7):2425-2429.
Bian Xia,Mi Liang. Development on genetic algorithm theory and its applications[J]. Computer Application Research,2010,27(7):2425-2429.
[17]Saha B,Goebel K. Battery data set[R]. California,USA:NASA Ames Prognostics Data Repository,2007.
[18]He Yijun,Shen Jiani,Shen Jifu,et al. State of health estimation of lithium-ion batteries:A multiscale Gaussian process regression modeling approach[J]. Aiche Journal,2015,61(5):1589-1600.
相似文献/References:
[1]李克婧,张小兵.改进型遗传算法在弹丸结构优化设计中的应用[J].南京理工大学学报(自然科学版),2009,(03):339.
LI Ke-jing,ZHANG Xiao-bing.Application of Improved Genetic Algorithm to Optimization Design of Projectile Structure[J].Journal of Nanjing University of Science and Technology,2009,(03):339.
[2]张俊芳,秦红霞,贾 晋,等.基于改进遗传算法的AGC机组优化组合研究[J].南京理工大学学报(自然科学版),2009,(06):801.
ZHANG Jun-fang,QIN Hong-xia,JIA Jin,et al.Optimization of Generator Unit Commitment Including AGC Based on Improved Genetic Algorithm[J].Journal of Nanjing University of Science and Technology,2009,(03):801.
[3]黄俊,徐越兰.碳钢焊条熔敷金属力学性能非线性神经网络组合预测[J].南京理工大学学报(自然科学版),2012,36(05):800.
HUANG Jun,XU Yue-lan.Nonlinear Combination Prediction of Mechanical Properties of CarbonSteel Electrode Deposited Metal Based on Neural Network[J].Journal of Nanjing University of Science and Technology,2012,36(03):800.
[4]门志国,彭秀艳,王兴梅,等.基于GA优化BP神经网络辨识的Volterra级数核估计算法[J].南京理工大学学报(自然科学版),2012,36(06):0.
MEN Zhi guo,PENG Xiu yan,WANG Xing mei,et al.Volterra Series Kernels Estimation Algorithm Based on GA Optimized BP Neural Network Identification[J].Journal of Nanjing University of Science and Technology,2012,36(03):0.
[5]王钟羡,郭晨海,刘 军,等.结构优化设计的猴王遗传算法[J].南京理工大学学报(自然科学版),2004,(04):346.
WANG Zhong xian,GUO Chen hai,LIU Jun,et al.Monkey-king Genetic Algorithms for Optimal Structural Design[J].Journal of Nanjing University of Science and Technology,2004,(03):346.
[6]李纯莲,王希诚,赵金城.基于浮点数编码的信息熵控制多种群遗传算法[J].南京理工大学学报(自然科学版),2004,(05):453.
LI Chun-lian,WANG Xi-cheng,ZHAO Jin-cheng.Multi-population Genetic Algorithm Controlled by Information Entropy Based on Floating-point Coding[J].Journal of Nanjing University of Science and Technology,2004,(03):453.
[7]张金萍,等.一种动态种群不对称交叉的新型遗传算法[J].南京理工大学学报(自然科学版),2007,(04):444.
ZHANG Jin-ping,LIU Jie,LI Yun-gong.Novel Dynamic Population and Anisomerous Crossover Genetic Algorithm[J].Journal of Nanjing University of Science and Technology,2007,(03):444.
[8]康明才.基于遗传算法的变电站电压-无功综合控制[J].南京理工大学学报(自然科学版),2002,(05):490.
KangMingcai.Control Strategy of Voltage and Reactive Power in Substation Based on Genetic Algorithm[J].Journal of Nanjing University of Science and Technology,2002,(03):490.
[9]杨云,徐永红,刘凤玉.一种连续探索型自适应遗传算法及其应用[J].南京理工大学学报(自然科学版),2002,(06):580.
YangYun XuYonghong LiuFengfu.A Self-adaptative Genetic Algorithm Based on Relay Search Method and Its Application[J].Journal of Nanjing University of Science and Technology,2002,(03):580.
[10]赖少发,刘华军.机动目标跟踪支持向量回归学习新方法[J].南京理工大学学报(自然科学版),2017,41(02):264.[doi:10.14177/j.cnki.32-1397n.2017.41.02.019]
Lai Shaofa,Liu Huajun.Novel approach in maneuvering target tracking based onsupport vector regression[J].Journal of Nanjing University of Science and Technology,2017,41(03):264.[doi:10.14177/j.cnki.32-1397n.2017.41.02.019]