Logical radial basis function networks a hybrid intelligent model for function approximation
Advances in Engineering Software
A fuzzy classifier with ellipsoidal regions for diagnosis problems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Expert Systems with Applications: An International Journal
Theoretical analysis of the shaft
Advances in Fuzzy Systems
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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.