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Ensemble learning based on policy optimization neural networks for capability assessment
F. Zhang; J. Li; Y. Wang; L. Guo; D. Wu; H. Wu and H. Zhao
2021
发表期刊Sensors
ISSN14248220
卷号21期号:17
摘要Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and index sensitivity. Aiming at the problem of overfitting and parameter optimization in neural network learning, the paper proposes an improved machine learning algorithmthe Ensemble Learning Based on Policy Optimization Neural Networks (ELPONN) with the policy optimization and ensemble learning. This algorithm presents an optimized neural network learning algorithm through different strategies evolution, and builds an ensemble learning model of multi-intelligent algorithms to assess the capability and analyze the sensitivity of the indexes. Through the assessment of capabilities, the algorithm effectively avoids parameter optimization from entering the minimum point in performance to improve the accuracy of equipment capability assessment, which is significantly better than previous neural network assessment methods. The experimental results show that the mean relative error is 4.10%, which is better than BP, GABP, and early stopping. The ELPONN algorithm has better accuracy and stability performance, and meets the requirements of capability assessment. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
DOI10.3390/s21175802
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收录类别SCI ; EI
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文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/65224
专题中国科学院长春光学精密机械与物理研究所
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F. Zhang,J. Li,Y. Wang,et al. Ensemble learning based on policy optimization neural networks for capability assessment[J]. Sensors,2021,21(17).
APA F. Zhang,J. Li,Y. Wang,L. Guo,D. Wu,&H. Wu and H. Zhao.(2021).Ensemble learning based on policy optimization neural networks for capability assessment.Sensors,21(17).
MLA F. Zhang,et al."Ensemble learning based on policy optimization neural networks for capability assessment".Sensors 21.17(2021).
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