Next Article in Journal
Identification of Two Commercial Pesticides by a Nanoparticle Gas-Sensing Array
Previous Article in Journal
W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets
Article

Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment

1
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, No. 3888, Dongnanhu Rd., Changchun 130033, China
2
University of Chinese Academy of Sciences, No. 19, Yuquan Rd., Beijing 100049, China
3
School of Aviation Operations and Services, Aviation University of Air Force, No. 2222, Dongnanhu Rd., Changchun 130022, China
4
PLA96901 Unit, No. 109, Beiqing Street, Haidian District, Beijing 100094, China
5
School of Computer Science, Jilin University, No. 2699, Qianjing Rd., Changchun 130012, China
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Alberto Avizzano
Sensors 2021, 21(17), 5802; https://doi.org/10.3390/s21175802
Received: 30 June 2021 / Revised: 31 July 2021 / Accepted: 16 August 2021 / Published: 28 August 2021
(This article belongs to the Section Physical Sensors)
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 algorithm—the 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. View Full-Text
Keywords: capability assessment; policy optimization; ensemble learning; artificial neural network; index sensitivity capability assessment; policy optimization; ensemble learning; artificial neural network; index sensitivity
Show Figures

Figure 1

MDPI and ACS Style

Zhang, F.; Li, J.; Wang, Y.; Guo, L.; Wu, D.; Wu, H.; Zhao, H. Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment. Sensors 2021, 21, 5802. https://doi.org/10.3390/s21175802

AMA Style

Zhang F, Li J, Wang Y, Guo L, Wu D, Wu H, Zhao H. Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment. Sensors. 2021; 21(17):5802. https://doi.org/10.3390/s21175802

Chicago/Turabian Style

Zhang, Feng, Jiang Li, Ye Wang, Lihong Guo, Dongyan Wu, Hao Wu, and Hongwei Zhao. 2021. "Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment" Sensors 21, no. 17: 5802. https://doi.org/10.3390/s21175802

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop