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The generalized regression neural network (GRNN) model of sound absorption coefficient of porous glass was built on data from 16 groups gained by experiments, where 12 groups were randomly selected as trained samples and the other 4 groups were as tested ones. This GRNN model which has two parameters, porosity and thickness as the inputs, was set the maximum iteration number 20, getting the optimal trained spread parameter  σ =0.1. The results showed that the average error of this model was 0.003, and this model has high precision and the prediction curve of the sound absorption coefficient was very similar to the experiments. The advantages of this method are simple, needing less trained samples, rapid and accurate.

Building Sound Absorption Performance Model of Porous Glass Based on GRNN