Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment
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 overfit...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/17/5802 |
| _version_ | 1797520792175509504 |
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| author | Feng Zhang Jiang Li Ye Wang Lihong Guo Dongyan Wu Hao Wu Hongwei Zhao |
| author_facet | Feng Zhang Jiang Li Ye Wang Lihong Guo Dongyan Wu Hao Wu Hongwei Zhao |
| author_sort | Feng Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:03:38Z |
| format | Article |
| id | doaj.art-0e4335de908f4c6881184ed9346874fb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:03:38Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0e4335de908f4c6881184ed9346874fb2023-11-22T11:12:39ZengMDPI AGSensors1424-82202021-08-012117580210.3390/s21175802Ensemble Learning Based on Policy Optimization Neural Networks for Capability AssessmentFeng Zhang0Jiang Li1Ye Wang2Lihong Guo3Dongyan Wu4Hao Wu5Hongwei Zhao6Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, No. 3888, Dongnanhu Rd., Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, No. 3888, Dongnanhu Rd., Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, No. 3888, Dongnanhu Rd., Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, No. 3888, Dongnanhu Rd., Changchun 130033, ChinaSchool of Aviation Operations and Services, Aviation University of Air Force, No. 2222, Dongnanhu Rd., Changchun 130022, ChinaPLA96901 Unit, No. 109, Beiqing Street, Haidian District, Beijing 100094, ChinaSchool of Computer Science, Jilin University, No. 2699, Qianjing Rd., Changchun 130012, ChinaCapability 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.https://www.mdpi.com/1424-8220/21/17/5802capability assessmentpolicy optimizationensemble learningartificial neural networkindex sensitivity |
| spellingShingle | Feng Zhang Jiang Li Ye Wang Lihong Guo Dongyan Wu Hao Wu Hongwei Zhao Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment Sensors capability assessment policy optimization ensemble learning artificial neural network index sensitivity |
| title | Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment |
| title_full | Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment |
| title_fullStr | Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment |
| title_full_unstemmed | Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment |
| title_short | Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment |
| title_sort | ensemble learning based on policy optimization neural networks for capability assessment |
| topic | capability assessment policy optimization ensemble learning artificial neural network index sensitivity |
| url | https://www.mdpi.com/1424-8220/21/17/5802 |
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