参考文献(References):
[1] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[J].Communications of the ACM,2017,60(6):84.
[2] BENGIO Y,COURVILLE A,VINCENT P.Representation learning:a review and new perspectives[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(8):1798.
[3] HOI S C H,WANG J L,ZHAO P L.Libol:a library for online learning algorithms[J].The Journal of Machine Learning Research,2014,15(1):495.
[4] CHEN K L,LEE C H,GARUDADRI H,et al.ResNEsts and DenseNEsts:block-based DNN models with improved representation guarantees[J].Advances in Neural Information Processing Systems,2021,34:3413.
[5] DAUPHIN Y N,PASCANU R,GULCEHRE C,et al.Identifying and attacking the saddle point problem in high-dimensional non-convex optimization[C]//NIPS’14:Proceedings of the 27th International Conference on Neural Information Processing Systems :Vol 2.New York:ACM,2014:2933.
[6] IOFFE S,SZEGEDY C.Batch normalization:accelerating deep network training by reducing internal covariate shift[EB/OL].(2015-11-11) [2024-05-10].https://doi.org/10.48550/arXiv.1502.03167.
[7] NAIR V,HINTON G E.Rectified linear units improve restricted boltzmann machines[C]//Proceedings of the 27th International Conference on Machine Learning (ICML-10),June 21-24,2010,Haifa,Israel.Madison:Omnipress,2010:807.
[8] SAHOO D,PHAM Q,LU J,et al.Online deep learning:learning deep neural networks on the fly[EB/OL].(2017-11-10)[2024-05-10].https://doi.org/10.48550/arXiv.1711.03705.
[9] ASHFAHANI A,PRATAMA M.Autonomous deep learning:continual learning approach for dynamic environments[C]//Proceedings of the 2019 SIAM international conference on data mining,May 2-4,2019,Calgary,Canada.Philadelphia:SIAM,2019:666.
[10] YANG Y,ZHOU D W,ZHAN D C,et al.Adaptive deep models for incremental learning:considering capacity scalability and sustainability[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,August 4-8,2019,Anchorage,USA.New York:ACM,2019:74.
[11] HUANG G B,ZHU Q Y,SIEW C K.Extreme learning machine:a new learning scheme of feedforward neural networks[C]//2004 IEEE International Joint Conference on Neural Networks,July 25-29,2004,Budapest,Hungary.Piscataway:IEEE,2004:2.
[12] HUANG G B,LIANG N Y,RONG H J,et al.On-line sequential extreme learning machine[J].Computational Intelligence,2005,2005:232.
[13] IGELNIK B,PAO Y H.Stochastic choice of basis functions in adaptive function approximation and the functional-link net[J].IEEE Transactions on Neural Networks,1995,6(6):1320.
[14] SHIVA S,HU M H,SUGANTHAN P N.Online learning using deep random vector functional link network[J].Pattern recognition,2022,129:108744.
[15] XUE H,REN Z.Sketch discriminatively regularized online gradient descent classification[J].Applied Intelligence,2020,50(5):1367.
[16] MARQUARDT D W.An algorithm for least-squares estimation of nonlinear parameters[J].Journal on the Society for Industrial and Applied Mathematics,1963,11(2):431.
[17] SONG Q,MI Y X,LAI W X.A novel variable forgetting factor recursive least square algorithm to improve the anti-interference ability of battery model parameters identification[J].IEEE Access,2019,7:61548.
[18] GOLUB G H,HANSEN P C,O’LEARY D P.Tikhonov regularization and total least squares[J].SIAM Journal on Matrix Analysis and Applications,1999,21(1):185.
[19] YING Y M,PONTIL M.Online gradient descent learning algorithms[J].Foundations of Computational Mathematics,2008,8:561.
[20] MASTERS D,LUSCHI C.Revisiting small batch training for deep neural networks[EB/OL].(2018-04-20)[2024-05-09].https://doi.org/10.48550/arXiv.1804.07612.
[21] CHEN W W,TAN D K,ZHAO L F.Vehicle sideslip angle and road friction estimation using online gradient descent algorithm[J].IEEE Transactions on Vehicular Technology,2018,67(12):11475.
[22] JANSSON P A.Neural networks:an overview[J].Analytical Chemistry,1991,63(6):357A.
[23] BISHOP C M.Neural networks and their applications[J].Review of Scientific Instruments,1994,65(6):1803.
[24] FRíAS-BLANCO I,CAMPO-áVILA J del,RAMOS-JIMéNEZ G,et al.Online and non-parametric drift detection methods based on Hoeffding’s bounds[J].IEEE Transactions on Knowledge and Data Engineering,2014,27(3):810.
[25] LU J,LIU A J,DONG F,et al.Learning under concept drift:a review[J].IEEE Transactions on Knowledge and Data Engineering,2018,31(12):2346.
[26] LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278.
[27] BAKHSHI S,GHAHRAMANIAN P,BONAB H,et al.A broad ensemble learning system for drifting stream classification[J].IEEE Access,2003,11:89315.