Abstract: A flies optimization algorithm (FOA) is used to optimize the spread value of generalized
regression neural network (GRNN). This method takes advantages of FOA in fast convergence and GRNN
in few parameters, and is compared with neural network prediction models (BP and ELMAN) for a
comparative study on prediction features of the vibration responses of overflow structure on the roof of a
hydropower station dam. The comparison of three models concludes that the GRNN based on FOA has both
prediction ability and learning speed superior to BP or ELMAN. It also shows the feasibility of FOA-GRNN
in vibration predictions that enhances intelligence in monitoring hydraulic structure.