Neural network modeling of the annoyance perception of cabin noise in passenger cars with hybrid algorithm optimization

被引：0

作者：

Qian K. ^{[1
]}

Tan J. ^{[1
]}

Shen Z. ^{[1
]}

Li H. ^{[1
]}

Liu K. ^{[1
]}

Wang Y. ^{[1
]}

Zhao J. ^{[1
]}

机构：

[1] School of Mechanical Engineering, Dalian University of Technology, Dalian

来源：

Shengxue Xuebao/Acta Acustica | 2024年 / 49卷 / 02期

关键词：

Cabin noise; Genetic algorithm; Neural network; Sound quality; Sparrow search algorithm;

D O I：

10.12395/0371-0025.2023262

中图分类号：

学科分类号：

摘要：

Addressing the issue of low accuracy in traditional quantitative models for the perception of annoyance due to noise in vehicles, an evaluation method is proposed using a neural network model optimized by a hybrid algorithm to predict the perceived annoyance of interior noise. This hybrid algorithm integrates the sparrow search algorithm (SSA) and genetic algorithm (GA) to optimize the back propagation (BP) neural network. Based on the subjective and objective sound quality evaluation data, an objective quantification model of interior noise annoyance using the SSA-GA-BP network is established and compared with BP, GA-BP, and SSA-BP models. The results show that the SSA-GA-BP model achieves higher prediction accuracy, closer to subjective evaluation figures, and stronger generalization capabilities, making it a viable alternative to traditional subjective experiments in sound quality evaluation. © 2024 Science Press. All rights reserved.

引用

页码：254 / 262

页数：8

共 23 条

[1] 38, 9, pp. 1120-1125, (2016)
[2] 33, (2014)
[3] Xie L P, Lu C H, Liu Z, Et al., Study of electroencephalograph-based evaluation method of car sound quality, J. Comput. Inf. Sci. Eng, 23, 2, (2023)
[4] Gao Y H, Qian K, Liang J, Et al., Interior sound quality evaluation model of heavy commercial vehicles, J. Vibroeng, 18, 1, pp. 595-605, (2016)
[5] Wang Z, Li P, Liu H, Et al., Objective sound quality evaluation for the vehicle interior noise based on responses of the basilar membrane in the human ear, Appl. Acoust, 172, (2021)
[6] Li F, Zuo Y Y., Sound quality evaluation control of car interior noise, Appl. Mech. Mater, 415, pp. 569-573, (2013)
[7] Qian K, Hou Z, Sun D., Sound quality estimation of electric vehicles based on GA-BP artificial neural networks, Appl. Sci, 10, 16, pp. 55-67, (2020)
[8] He Z H., Combinatorial optimization analysis of the production process of C4 olefins from ethanol based on the PSO-BP algorithm, J Comb. Optim, 45, 5, (2023)
[9] Pan X P, Niu Y W, Yu Z, Et al., Parameter calibration method of clustered-particle logic concrete DEM model using BP neural network-particle swarm optimisation algorithm (BP-PSO) inversion method, Eng. Fract. Mech, 292, (2023)
[10] Pei Z W, Liu K M, Zhang S, Et al., Optimized EKF algorithm using TSO-BP neural network for lithium battery state of charge estimation, J. Energy Storage, 73, (2023)

← 1 2 3 →