C-loss based extreme learning machine for estimating power of small-scale turbojet engine

As a high-efficiency training method for single layer feedforward neural networks, extreme learning machine (ELM) has drawn much interest recently, but its robustness is not good due to the adoption of the square loss function. Hence, the convex loss function in ELM is replaced with a nonconvex loss...

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Bibliographic Details
Published in:Aerospace science and technology 2019-06, Vol.89, p.407-419
Main Authors: Zhao, Yong-Ping, Tan, Jian-Feng, Wang, Jian-Jun, Yang, Zhe
Format: Article
Language:English
Subjects:
Machine learning
Single layer feedforward neural networks
Small-scale turbojet engine
Unmanned aerial vehicles
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Summary:As a high-efficiency training method for single layer feedforward neural networks, extreme learning machine (ELM) has drawn much interest recently, but its robustness is not good due to the adoption of the square loss function. Hence, the convex loss function in ELM is replaced with a nonconvex loss function, i.e., the C-loss function, so a novel algorithm, called C-loss based ELM (CELM), is proposed in this paper. According to the experimental results on a toy example and two benchmark data sets, CELM performs better than the other algorithms with respect to the generalization performance. To be more important, when CELM is used to estimate power of small-scale turbojet engine, it also dominates the other algorithms, which makes the development of the potential control structure, viz., the direct power control, for the unmanned aerial vehicles more feasible.
ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2019.04.023