Volume 37, Number 1, February 2019
|Page(s)||57 - 62|
|Published online||03 April 2019|
Building Sound Absorption Performance Model of Porous Glass Based on GRNN
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
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.
Key words: generalized regression neural network / sound absorption coefficient / porous glass
关键字 : 广义回归神经网络 / 吸声系数 / 多孔玻璃
© 2019 Journal of Northwestern Polytechnical University
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