A genetic fuzzy radial basis function neural network for structural health monitoring of composite laminated beams

摘要

In this paper, a new neural network learning procedure, called genetic fuzzy hybrid learning algorithm (GFHLA) is proposed for training the radial basis function neural network (RBFNN). The method combines the genetic algorithm and fuzzy logic to optimize the centers and widths of the RBFNN, and the linear least-squared method is used to adjust the neural network connection weights. The modal frequencies of a glass/epoxy laminates beam with varying assumed delamination sizes and locations were computed using finite element method and fed into the genetic fuzzy RBF neural network to predict the delamination location and its extent. The simulation demonstrates that the neural network based on GFHLA is robust and promising.