Prediction of sound absorption property of metal rubber using general regression neural network
2018
Metal rubber (MR) is an excellent sound absorption material which can be utilized
in extremely harsh environments. Traditional experimental studies are incomplete
for MR with random structural parameters. In this article, the general regression
neural network (GRNN) method is developed to comprehensively predict the sound
absorption behaviors of MR with random structural parameters. Sixty samples are
utilized and divided into training and test set. Training set contains 50 samples to
establish the GRNN model. Input training parameters include the porosity, wire
diameter and thickness, while the target dates consist of sound absorption coefficients at six central frequencies as well as their average values. The remaining
10 samples constitute the testing set; sound absorption coefficients can be obtained
by inputting their structure parameters. Results indicate that the proposed approach
is reliable to design and predict the sound absorption properties of MR in engineering field. © 2018 Institute of Noise Control Engineering.
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