基于极限学习的弹丸阻力系数辨识

气动参数对弹丸的弹道特性起决定性作用,在无控弹丸研制过程中,准确获取弹丸气动参数是减小落点散布、提高打击精度的基础与关键.为了进一步提高弹丸阻力系数的辨识精度,基于质点弹道方程,通过数值仿真产生弹道数据,利用极限学习方法在多种噪声环境下实现弹丸阻力系数弹道大数据辨识.该方法随机产生输入权重以及隐含层神经元阈值,随机生成...

Projectile Resistance Coefficient Identification Based on Extreme Learning Machine

Aerodynamic parameters play a decisive role in the ballistic characteristics of the projectile. Accurate acquisition of projectile aerodynamic parameters is the key to reducing the spread of drop points and improving strike accuracy in the development process of uncontrolled projectiles. In order to further improve the identification accuracy of the projectile resistance coefficient,based on the mass point ballistic equation,the ballistic data generated through numerical simulation,and the extreme learning machine was used to identify the ballistic resistance coefficient under three kinds of noise conditions. By randomly generating input weights and hidden layer neuron thresholds,the randomly generated input weights and hidden layer neuron thresholds are independent of each other and do not require iterative update,the method reduces the identification time and improves identification accuracy of traditional identification methods. Based on the least square principle,the Moor-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 resistance coefficient was accurately identified. Comparing the results of extreme learning machine with the traditional BP neural network method and Maximum likelihood method,the results show that the proposed method has higher identification accuracy,faster convergence speed and stronger anti-interference ability. It can effectively identify the projectile resistance coefficient under the high-noise environment,which can meet the practical needs of engineering.