Abstract
Aerodynamic parameters play a decisive role in the ballistic characteristics of the projectile. How to accurately obtain the aerodynamic parameters of the projectile is an important task in the development process of the projectile. In order to further improve the identification accuracy of the projectile drag coefficient, this paper generates huge ballistic data through numerical simulation and uses the extreme learning method to identify the ballistic drag coefficient under three kinds of noise conditions. The method avoids the iterative updating process of weights and thresholds by randomly generating the input weights and threshold values of hidden layer neurons and overcomes the problem of long identification time of the traditional back propagation (BP) neural network algorithm. Based on the least squares principle, the Moore–Penrose generalized inverse matrix of the hidden layer output matrix was solved to determine the optimal output weight of the network, and then, the projectile drag coefficient was accurately identified. Comparing the extreme learning method with the traditional BP neural network method, the results show that the proposed method has higher identification accuracy and faster convergence speed and can effectively identify the projectile drag coefficient, which can meet the practical needs of engineering.
Highlights
There are various forms of modern war
The research of the aerodynamic parameter identification method can effectively improve the accuracy of the uncontrolled ejection table, reduce the design difficulty of the controlled missile guidance control system, and improve the strike accuracy
The traditional aerodynamic parameter identification problem of missiles based on the Champ–Kirk method and maximum likelihood method uses one trajectory to identify the aerodynamic parameters, and the identification results are different for different trajectories, which is not the real global identification
Summary
There are various forms of modern war. As the main weapon, the ballistic performance of missiles and rockets is required to be higher. Based on the maximum likelihood criterion, Wang and co-workers used the genetic algorithm as the aerodynamic identification algorithm of missiles, which greatly reduced the complexity of aerodynamic parameter identification calculation and improved the accuracy and reliability of aerodynamic parameter identification. The extreme learning machine (ELM) is a simple and efficient neural network structure proposed by Huang from the Nanyang University of Technology in Singapore It has been widely used in cloud computing, data visualization, and random projection because of its good generalization performance, not easy to fall into local optimization, good robustness, and few parameters to be adjusted manually.. The traditional aerodynamic parameter identification problem of missiles based on the Champ–Kirk method and maximum likelihood method uses one trajectory to identify the aerodynamic parameters, and the identification results are different for different trajectories, which is not the real global identification. The identification result has the advantages of high accuracy and high speed
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