9 Papers
47 Citations
Pan Yingting is an academic researcher from Nanjing University of Aeronautics and Astronautics. The author has contributed to research in topics: Extreme learning machine & Support vector machine. The author has an hindex of 6, co-authored 9 publications.
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Papers
Least squares support vector machine for class imbalance learning and their applications to fault detection of aircraft engine
TL;DR: The effectiveness of the two algorithms presented in this paper is confirmed with experimental results on various real-world imbalanced datasets and experiments on aircraft engine indicate that theTwo algorithms can be selected as candidate techniques for fault detection of aircraft engine.
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A robust extreme learning machine for modeling a small-scale turbojet engine
TL;DR: A robust extreme learning machine is proposed that minimizes both the mean and variance of modeling errors in the objective function to overcome the bias-variance dilemma and provides a candidate technique for modeling real systems.
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Retargeting extreme learning machines for classification and their applications to fault diagnosis of aircraft engine
TL;DR: Two algorithms are proposed to improve the real time performance of ELM from a viewpoint of data structure and need fewer hidden nodes to reach the same classification performance, which means the better real time.
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A proposed self-organizing radial basis function network for aero-engine thrust estimation
Zhi-Qiang Li,Yong-Ping Zhao,Zhi-Yuan Cai,Peng-Peng Xi,Pan Yingting,Huang Gong,Tianhong Zhang +6 more
TL;DR: A new algorithm to construct self-organizing radial basis function neural networks (RBFNNs) for aero-engine thrust estimation that can not only optimize centers and network size of the RBFNN but also automatically determine the connection weights.
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Feature selection of generalized extreme learning machine for regression problems
TL;DR: Two greedy learning algorithms are proposed that enhance the generalization performance and simultaneously reduce the testing time compared to the original GELM, and both can select appropriate input features to construct the p-order reduced polynomial function as output weights for G ELM.
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