基于果蝇算法优化广义回归神经网络的机枪枪管初速衰减建模与预测

Modeling and Prediction of Muzzle Velocity Degradation of Machine Gun Based on FOAGRNN

机枪枪管初速衰减预测是一个复杂的非线性问题。广义回归神经网络方法被广泛应用于非线性问题的建模,但其平滑因子取值对神经网络的预测性能有较大影响。采用果蝇算法对广义回归神经网络的参数进行优化选取,提出了基于果蝇算法优化广义回归神经网络的机枪枪管初速衰减建模方法。基于机枪枪管初速衰减试验数据,建立在不同使用环境下随着累计射弹量的增加,以初速降为特征量的机枪枪管初速衰减预测模型,预测结果与试验结果基本一致,证实了所提方法的可行性。通过与未经优化的广义回归神经网络方法和反向传播神经网络方法建立的预测模型进行比较,其性能明显优于另外两种方法,验证了基于果蝇算法优化的广义回归神经网络方法在建立机枪枪管初速衰减模型中的有效性。

Muzzle velocity degradation prediction of machine gun is a complicated non-linear problem. Generalized regression neural network （GRNN） has been widely used in the modeling of the non-linear problems, but GRNN has rarely been used to predict the muzzle velocity degradation of machine gun. Since the smoothing factor of GRNN obviously affects the prediction performance of neural network, the fruit fly optimization algorithm is used to automatically select the parameters of GRNN. A method to mod- el a muzzle velocity degradation based on general regression neural network with fruit fly optimization al- gorithm （FOAGRNN） is proposed. A prediction model is established based on the experimental data of muzzle velocity degradation, in which the muzzle velocity degradation is taken as characteristic quantity. The predicted results are basically consistent with the experimental results. The research result shows that FOAGRNN model outperforms GRNN model with default parameter and BPNN prediction model in the prediction of muzzle velocity degradation.