Application of evolutionary neural networks to grid-stiffened composite structure design

Based on Kolmogorov theorem, the global nonlinear mapping relationship between structural design parameters (input) and structural response parameters (output) was realized by using evolutionary neural networks (ENN), which can replace massive finite element calculation during actual optimization process so as to improve optimization efficiency. Taking genetic algorithm (GA) as the optimization procedure and the neural network buckling response surface as main constraints, the optimal design of grid-stiffened composite panel under axial compressive loads was investigated. The results show that with the same FEM sample data, evolutionary neural networks can get more accurate mapping model than traditional BP neural network through self-adaptive adjustment grid structure and weight value. The ENN-GA algorithm provides an efficient approach to the structure optimization design of large complex composite.