Citation: | ZHU Min, XU Aiqiang, CHEN Qiangqiang, et al. An improved KELM based online condition prediction method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(7): 1370-1379. doi: 10.13700/j.bh.1001-5965.2018.0685(in Chinese) |
In order to curb kernel matrix expansion and track the time-varying dynamic characteristics when kernel extreme learning machine (KELM) is applied to online condition prediction, a sparse KELM online prediction algorithm with forgetting factor is proposed. By introducing forgetting factor, a new objective function is constructed, which makes every element in sparse dictionary has different weights related to timestamp and ensures the effective tracking of the dynamic changes. By minimizing the fast leave-one-out cross-validation (FLOO-CV) error, key nodes with predetermined size are selected to form a dictionary. At the same time, the online recursive updating of model parameters is realized based on the elementary transformation of matrix and the inverse formula of block matrix. The proposed algorithm is compared with the recently proposed three online sequential KELM algorithms. The experimental results of aero-engine condition prediction show that the average training time of the proposed algorithm on six monitoring items is reduced by 7.5%, 62.0% and 81.9% respectively, and the average prediction accuracy is improved by 44.0%, 19.9% and 50.9% respectively.
[1] |
TIAN Z, QIAN C, GU B, et al.Electric vehicle air conditioning system performance prediction based on artificial neural network[J].Applied Thermal Engineering, 2015, 89:101-104. doi: 10.1016/j.applthermaleng.2015.06.002
|
[2] |
HUANG G B, ZHU Q Y, SIEW C K.Extreme learning machine:Theory and application[J].Neurocomputing, 2006, 70(1-3):489-501. doi: 10.1016/j.neucom.2005.12.126
|
[3] |
YIN G, ZHANG Y T, LI Z N, et al.Online fault diagnosis method based on incremental support vector data description and extreme learning machine with incremental output structure[J].Neurocomputing, 2014, 128:224-231. doi: 10.1016/j.neucom.2013.01.061
|
[4] |
BILAL M, LIN Z P, LIU N.Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift[J].Neurocomputing, 2015, 149:316-329. doi: 10.1016/j.neucom.2014.03.075
|
[5] |
LIANG N Y, HUANG G B, SARATCHANDRAN P, et al.A fast and accurate online sequential learning algorithm for feedforward networks[J].IEEE Transactions on Neural Networks, 2006, 17(6):1411-1423. doi: 10.1109/TNN.2006.880583
|
[6] |
HUANG G B, ZHOU H, DING X, et al.Extreme learning machine for regression and multiclass classification[J].IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics, 2011, 42(2):513-529.
|
[7] |
SCARDAPANE S, COMMINIELLO D, SCARPINITI M, et al.Online sequential extreme learning machine with kernels[J].IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(9):2214-2220. doi: 10.1109/TNNLS.2014.2382094
|
[8] |
GUO L, HAO J H, LIU M.An incremental extreme learning machine for online sequential learning problems[J].Neurocomputing, 2014, 128:50-58. doi: 10.1016/j.neucom.2013.03.055
|
[9] |
YU X, RASHID M, FENG J, et al.Online glucose prediction using computationally efficient sparse kernel filtering algorithms in type-1 diabetes[J].IEEE Transactions on Control Systems Technology, 2018, 99:1-13.
|
[10] |
HAN M, ZHANG S, XU M, et al.Multivariate chaotic time series online prediction based on improved kernel recursive least squares algorithm[J].IEEE Transactions on Cybernetics, 2018, 49(4):1160-1172.
|
[11] |
HONEINE P.Analyzing sparse dictionaries for online learning with kernels[J].IEEE Transactions on Signal Processing, 2015, 63(23):6343-6353. doi: 10.1109/TSP.2015.2457396
|
[12] |
ZHOU X R, LIU Z J, ZHU C X.Online regularized and kernelized extreme learning machines with forgetting mechanism[J].Mathematical Problems in Engineering, 2014, 2014:1-11.
|
[13] |
ZHOU X R, WANG C S.Cholesky factorization based online regularized and kernelized extreme learning machines with forgetting mechanism[J].Neurocomputing, 2016, 174:1147- 1155. doi: 10.1016/j.neucom.2015.10.033
|
[14] |
RICHARD C, BERMUDEZ J C M, HONEINE P.Online prediction of time series data with kernels[J].IEEE Transactions on Signal Processing, 2009, 57(3):1058-1067. doi: 10.1109/TSP.2008.2009895
|
[15] |
张伟, 许爱强, 高明哲.基于稀疏核增量超限学习机的机载设备在线状态预测[J].北京航空航天大学学报, 2017, 43(10):2089-2098.
ZHANG W, XU A Q, GAO M Z.Online condition prediction of avionic devices based on sparse kernel incremental extreme learning machine[J].Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(10):2089-2098(in Chinese).
|
[16] |
张伟, 许爱强, 高明哲.一种基于积累一致性测量的在线状态预测算法[J].上海交通大学学报, 2017, 51(11):1391-1398.
ZHANG W, XU A Q, GAO M Z.An online condition prediction algorithm based on cumulative coherence measurement[J].Journal of Shanghai Jiaotong University, 2017, 51(11):1391-1398(in Chinese).
|
[17] |
ZHANG W, XU A Q, PING D F, et al.An improved kernel-based incremental extreme learning machine with fixed budget for nonstationary time series prediction[J].Neural Computing & Applications, 2019, 31(3):637-652.
|
[18] |
LIM J S, LEE S, PANG H S, et al.Low complexity adaptive forgetting factor for online sequential extreme learning machine (OS-ELM) for application to nonstationary system estimations[J].Neural Computing & Applications, 2013, 22(3-4):569- 576.
|
[19] |
郭威, 徐涛, 于建江, 等.基于M-estimator与可变遗忘因子的在线贯序超限学习机[J].电子与信息学报, 2018, 40(6):94-101.
GUO W, XU T, YU J J, et al.Online sequential extreme learning machine based on m-estimator and variable forgetting factor[J].Journal of Electronics & Information Technology, 2018, 40(6):94-101(in Chinese).
|
[20] |
张英堂, 马超, 李志宁, 等.基于快速留一交叉验证的核极限学习机在线建模[J].上海交通大学学报, 2014, 48(5):641-646.
ZHANG Y T, MA C, LI Z N, et al.Online modeling of kernel extreme learning machine based on fast leave-one-out cross-validation[J].Journal of Shanghai Jiaotong University, 2014, 48(5):641-646(in Chinese).
|
[21] |
HUYNH H T, WON Y.Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks[J].Pattern Recognition Letters, 2011, 32(14):1930-1935. doi: 10.1016/j.patrec.2011.07.016
|
[1] | CHEN Q,AN C,XIE C C,et al. Large deformation prediction and geometric nonlinear aeroelastic analysis based on machine learning algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics,2025,51(3):943-952 (in Chinese). doi: 10.13700/j.bh.1001-5965.2023.0111. |
[2] | HAI Chao, TIAN Xin, ZHANG Hong, TAN Da-long, HE Yi-xin, MENG Fan-yong, YANG Min. A Deep Learning-Based Dual-Domain Information Method for CT Metal Artifact Reduction[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0753 |
[3] | MA Qing-lu, DING Xue-qin, HUANG Xiao-xiao, ZOU Zheng. 3D point cloud segmentation method of road scene based on adaptive graph convolution[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0686 |
[4] | HE Chi-yuan, CHENG Shao-xu, XU Lin-feng, MENG Fan-man, WU Qing-bo. A Continual Learning Method Based on Differential Feature Distillation for Multimodal Network[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0369 |
[5] | YAO Yougui, LI Shujie, CHEN Zhangyi, XUE Feng. Autism spectrum disorder detection based on multi-modal graph learning[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2024.0467 |
[6] | WANG Dong, CUI Tianshu, JI Libin, HUANG Yonghui, ZHU Yan. Automatic modulation classification based on transfer learning[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2024.0231 |
[7] | HOU Z Q,MA J Y,HAN R X,et al. A fast long-term visual tracking algorithm based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(8):2391-2403 (in Chinese). doi: 10.13700/j.bh.1001-5965.2022.0645. |
[8] | YANG Huixin, WANG Xu, LI Xiang. A prediction method for solid divert and attitude control system performance based on deep neural network[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2024.0182 |
[9] | LIU Y H,HUANG Y,TAN H,et al. On-line prediction method of wing flexible baseline based on autoregressive model[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(11):3426-3433 (in Chinese). doi: 10.13700/j.bh.1001-5965.2022.0865. |
[10] | XIA J W,LIU Z K,ZHU X F,et al. A coordinated rendezvous method for unmanned surface vehicle swarms based on multi-agent reinforcement learning[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(12):3365-3376 (in Chinese). doi: 10.13700/j.bh.1001-5965.2022.0088. |
[11] | BAI Jing-bo, CHEN Yu, XIE Shi-yu, DAI Xin-wei. Design Method for Modulation Strategy of a Single-Inductor Multi-Port Converter Based on Reinforcement Learning[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0302 |
[12] | LIU Ren-di, JIANG Ju, ZHANG Zhe, LIU Xiang. Direct lift control technology of carrier aircraft landing based on reinforcement learning[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0403 |
[13] | WANG Xiangzhang, WANG He, XU Bohao. Hard landing risk prediction of civil aircraft based on GBDT-GS method[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0443 |
[14] | SUN X T,CHENG W,CHEN W J,et al. A visual detection and grasping method based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(10):2635-2644 (in Chinese). doi: 10.13700/j.bh.1001-5965.2022.0130. |
[15] | GAO H T,CHEN Y X. A machine learning based method for lithium-ion battery state of health classification and prediction[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(12):3467-3475 (in Chinese). doi: 10.13700/j.bh.1001-5965.2022.0154. |
[16] | ZHU Jiazheng, WANG Cong, LI Xinkai, DONG Yingchao, ZHANG Hongli. A deep reinforcement learning based discrete state transition algorithm for fuzzy flexible job shop scheduling[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0211 |
[17] | CHEN H,BAI J,YIN C T,et al. Behavior based MOOC user dropout predication framework[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):74-82 (in Chinese). doi: 10.13700/j.bh.1001-5965.2021.0188. |
[18] | YANG Shang-hang, XU Guo-ning, JIA Zhong-zhen, LI Yong-xiang, ZHUANG Chun-yu, YANG Yan-chu. Research on wireless charging coil location method of aircraft based on machine learning[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023-0006 |
[19] | JIANG Hao, LIU Jixin, DONG Xinfang. Dynamic collaborative sequencing for departure flights based on traffic state[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(10): 2048-2060. doi: 10.13700/j.bh.1001-5965.2021.0066 |
[20] | YIN Zengyuan, CAI Yuanwen, REN Yuan, WANG Weijie, CHEN Xiaocen, YU Chunmiao. Decoupled active disturbance rejection control method for magnetically suspended rotor based on state feedback[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(7): 1210-1221. doi: 10.13700/j.bh.1001-5965.2021.0021 |
1. | 李海君,王文双,赵建忠. 基于FA-RBF神经网络的导弹导引系统状态预测. 弹箭与制导学报. 2023(01): 1-7 . ![]() | |
2. | 戴金玲,许爱强,申江江,王树友. 基于OCKELM与增量学习的在线故障检测方法. 航空学报. 2022(03): 378-389 . ![]() | |
3. | 李海君,宋超,赵建忠. 基于CA-RBF神经网络的导弹健康状态预测. 航空兵器. 2022(05): 107-113 . ![]() | |
4. | 戴金玲,吴明辉,刘星,李睿峰. 核极限学习机的在线状态预测方法综述. 兵器装备工程学报. 2021(06): 12-19 . ![]() | |
5. | 戴金玲,许爱强,于超,吴阳勇. 基于多元KELM的发动机状态在线预测模型. 北京航空航天大学学报. 2021(11): 2277-2286 . ![]() | |
6. | 刘星,熊厚情,赵建印,朱敏. 基于改进稀疏KELM的在线非平稳动态系统状态预测方法. 系统工程与电子技术. 2020(09): 2022-2032 . ![]() | |
7. | 朱良,谭继文,张义清. 断丝状态下的钢丝绳故障诊断. 煤矿机械. 2019(09): 160-163 . ![]() | |
8. | 孙永泽,陆忠华. 基于超限学习机与随机响应面方法的深度学习超参数优化算法. 高技术通讯. 2019(12): 1165-1174 . ![]() |