Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment

Version 1 : Received: 24 June 2021 / Approved: 28 June 2021 / Online: 28 June 2021 (14:19:11 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Sensors 2021, 21, 5802
DOI: 10.3390/s21175802

Abstract

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 over-fitting 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) algorithm with the policy optimization and ensemble learning. This algorithm presents optimized neural network learning algorithm through different strategies evolution, and builds an ensemble learning model of multi-intelligent algorithms to assessment 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.

Keywords

Policy Optimization; Ensemble Learning; Artificial Neural Network; Index Sensitivity

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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