The Application of GA-BP Neural Network on Parameter Inversion of Wedge

Abstract

An inversion model based on neural network combined with genetic algorithm was established to obtain the material parameters of an unknown wedge. Firstly, a back propagation (BP) neural network was introduced and the genetic algorithm was implied to optimize the initial weights and thresholds of the BP neural network. Wedge wave dispersion curves with different angles, density and young’s modulus were obtained by simulation. Then the phase velocity of first-order mode in the anti-symmetrical flexural mode was chosen as the inputs of our inversion model, the corresponding parameters were taken as the outputs. The first-order mode data measured from samples were used to test the validity. It is found that the inversion models can inverse angle, density and young’s modulus simultaneously. Compared with the single BP neural network, it has the advantages of fast convergence speed and high precision.