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Yong-Ping Zhao
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2020 – today
- 2024
- [j31]Yong-Ping Zhao, Wen Cai:
The perceptron algorithm with uneven margins based transfer learning for turbofan engine fault detection. Eng. Appl. Artif. Intell. 127(Part A): 107249 (2024) - 2023
- [j30]Qiankun Hu, Yong-Ping Zhao, Yuqiang Wang, Pei Peng, Lihua Ren:
Remaining Useful Life Estimation in Prognostics Using Deep Reinforcement Learning. IEEE Access 11: 32919-32934 (2023) - [j29]Qiankun Hu, Yong-Ping Zhao, Lihua Ren:
Novel Transformer-Based Fusion Models for Aero-Engine Remaining Useful Life Estimation. IEEE Access 11: 52668-52685 (2023) - [j28]Yu-Qiang Wang, Yong-Ping Zhao:
A novel inter-domain attention-based adversarial network for aero-engine partial unsupervised cross-domain fault diagnosis. Eng. Appl. Artif. Intell. 123(Part C): 106486 (2023) - [j27]Pei Peng, Yong-Ping Zhao:
Robust semi-supervised discriminant embedding method with soft label in kernel space. Neural Comput. Appl. 35(11): 8601-8623 (2023) - 2022
- [j26]Bing Li, Yong-Ping Zhao, Yao-Bin Chen:
Learning transfer feature representations for gas path fault diagnosis across gas turbine fleet. Eng. Appl. Artif. Intell. 111: 104733 (2022) - [j25]Bing Li, Yong-Ping Zhao:
Simultaneous fault diagnosis for aircraft engine using multi-label learning. J. Syst. Control. Eng. 236(7): 1355-1371 (2022) - 2021
- [j24]Yong-Ping Zhao, Yun-Long Xie, Zhi-Feng Ye:
A new dynamic radius SVDD for fault detection of aircraft engine. Eng. Appl. Artif. Intell. 100: 104177 (2021) - [j23]Huan Wu, Yong-Ping Zhao, Huijun Tan:
A novel neural network based on dynamic time warping and Kalman filter for real-time monitoring of supersonic inlet flow patterns. Eng. Appl. Artif. Intell. 102: 104258 (2021) - 2020
- [j22]Yong-Ping Zhao, Zhi-Qiang Li, Qian-Kun Hu:
A size-transferring radial basis function network for aero-engine thrust estimation. Eng. Appl. Artif. Intell. 87 (2020) - [j21]Yong-Ping Zhao, Gong Huang, Qian-Kun Hu, Bing Li:
An improved weighted one class support vector machine for turboshaft engine fault detection. Eng. Appl. Artif. Intell. 94: 103796 (2020) - [j20]Bing Li, Yong-Ping Zhao:
Group reduced kernel extreme learning machine for fault diagnosis of aircraft engine. Eng. Appl. Artif. Intell. 96: 103968 (2020)
2010 – 2019
- 2018
- [j19]Yong-Ping Zhao, Peng-Peng Xi, Bing Li, Zhi-Qiang Li:
Sparse kernel minimum squared error using Householder transformation and givens rotation. Appl. Intell. 48(2): 390-415 (2018) - [j18]Yong-Ping Zhao, Ying-Ting Pan, Fang-Quan Song, Liguo Sun, Tinghao Chen:
Feature selection of generalized extreme learning machine for regression problems. Neurocomputing 275: 2810-2823 (2018) - 2017
- [j17]Yong-Ping Zhao, Zhi-Qiang Li, Peng-Peng Xi, Dong Liang, Liguo Sun, Tinghao Chen:
Gram-Schmidt process based incremental extreme learning machine. Neurocomputing 241: 1-17 (2017) - [j16]Yong-Ping Zhao, Dong Liang, Zheng Ji:
A method of combining forward with backward greedy algorithms for sparse approximation to KMSE. Soft Comput. 21(9): 2367-2383 (2017) - 2016
- [j15]Yong-Ping Zhao, Ramón Huerta:
Improvements on parsimonious extreme learning machine using recursive orthogonal least squares. Neurocomputing 191: 82-94 (2016) - [j14]Yong-Ping Zhao:
Parsimonious kernel extreme learning machine in primal via Cholesky factorization. Neural Networks 80: 95-109 (2016) - 2015
- [j13]Yong-Ping Zhao, Kang-Kang Wang, Ye-Bo Li:
Parsimonious regularized extreme learning machine based on orthogonal transformation. Neurocomputing 156: 280-296 (2015) - [j12]Yong-Ping Zhao, Bing Li, Ye-Bo Li, Kang-Kang Wang:
Householder transformation based sparse least squares support vector regression. Neurocomputing 161: 243-253 (2015) - [j11]Yong-Ping Zhao, Bing Li, Ye-Bo Li:
An accelerating scheme for destructive parsimonious extreme learning machine. Neurocomputing 167: 671-687 (2015) - [j10]Yong-Ping Zhao, Kang-Kang Wang, Fu Li:
A pruning method of refining recursive reduced least squares support vector regression. Inf. Sci. 296: 160-174 (2015) - 2014
- [j9]Yong-Ping Zhao, Kang-Kang Wang, Jie Liu, Ramón Huerta:
Incremental kernel minimum squared error (KMSE). Inf. Sci. 270: 92-111 (2014) - 2013
- [j8]Yong-Ping Zhao, Jing Zhao, Min Zhao:
Twin least squares support vector regression. Neurocomputing 118: 225-236 (2013) - 2012
- [j7]Yong-Ping Zhao, Jian-Guo Sun, Zhong-Hua Du, Zhi-An Zhang, Yu-Chen Zhang, Haibo Zhang:
An improved recursive reduced least squares support vector regression. Neurocomputing 87: 1-9 (2012) - [j6]Yong-Ping Zhao, Jian-Guo Sun, Zhong-Hua Du, Zhi-An Zhang, Ye-Bo Li:
Online independent reduced least squares support vector regression. Inf. Sci. 201: 37-52 (2012) - 2011
- [j5]Yong-Ping Zhao, Jian-Guo Sun:
Multikernel semiparametric linear programming support vector regression. Expert Syst. Appl. 38(3): 1611-1618 (2011) - [j4]Yong-Ping Zhao, Zhong-Hua Du, Zhi-An Zhang, Haibo Zhang:
A fast method of feature extraction for kernel MSE. Neurocomputing 74(10): 1654-1663 (2011) - [j3]Yong-Ping Zhao, Jian-Guo Sun, Zhong-Hua Du, Zhi-An Zhang, Haibo Zhang:
Pruning least objective contribution in KMSE. Neurocomputing 74(17): 3009-3018 (2011) - 2010
- [j2]Yong-Ping Zhao, Jian-Guo Sun, Xian-Quan Zou:
Reducing samples for accelerating multikernel semiparametric support vector regression. Expert Syst. Appl. 37(6): 4519-4525 (2010) - [j1]Yong-Ping Zhao, Jian-Guo Sun:
Robust truncated support vector regression. Expert Syst. Appl. 37(7): 5126-5133 (2010)
Coauthor Index
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last updated on 2024-01-14 23:18 CET by the dblp team
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