ArticleMaterials (Basel, Switzerland)2022
Prediction of the Sound Absorption Coefficient of Three-Layer Aluminum Foam by Hybrid Neural Network Optimization Algorithm.
Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multi-objective optimization of corrugated microperforated panels for broadband sound absorption using adaptive sampling method.Scientific reports · 2026Article
- The Modern Numerical and Experimental Methods for the Sound Absorbing Characteristics of Dissipative Sound Absorbing Materials: A Review.Materials (Basel, Switzerland) · 2025Review
- Optimizing secure multimedia communication in embedded systems a parallel convolutional neural network approach to RIS and D2D resource allocation.Scientific reports · 2024Article
- Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling.Materials (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
The combination of multilayer aluminum foam can have high sound absorption coefficients (SAC) at low and medium frequencies, and predicting its absorption coefficient can help the optimal structural design. In this study, a hybrid EO-GRNN model was proposed for predicting the sound absorption coefficient of the three-layer composite structure of the aluminum foam. The generalized regression neural network (GRNN) model was used to predict the sound absorption coefficient of three-layer composite structural aluminum foam due to its outstanding nonlinear problem-handling capability. An equilibrium optimization (EO) algorithm was used to determine the parameters in the neuronal network. The prediction results show that this method has good accuracy and high precision. The calculation result shows that this proposed hybrid model outperforms the single GRNN model, the GRNN model optimized by PSO (PSO-GRNN), and the GRNN model optimized by FOA(FOA-GRNN). The prediction results are expressed in terms of root mean square error (RMSE), absolute error, and relative error, and this method performs well with an average RMSE of only 0.011.
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Registered trials
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