Structural dynamics optimization of gun based on neural networks and genetic algorithms

In order to study the optimization of muzzle disturbance, a new method of gun structural dynamics optimization based on nonlinear finite element method, experimental design, neural networks and genetic algorithms is proposed. A dynamic model of a large caliber gun is established based on the nonlinear finite element method, and the structural dynamics analysis of the gun is made based on experimental design. With experimental data as training samples, a back-propagation (BP) neural network is established to simulate the nonlinear mapping between the structural parameters and muzzle disturbance index based on Bayesian regularization algorithm. The optimal objective function of muzzle disturbance is constructed, the genetic algorithms is applied to solve the objective function, and the optimal design for structural parameters of the gun is realized. The results show that nonlinear relationship between the structural parameters and muzzle disturbance index established by the method is proved to be highly reliable, and the method is accurate and feasible to optimize the muzzle disturbance.