Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model

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Journal of Transportation Technologies

ISSN Print: 2160-0473
ISSN Online: 2160-0481
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"Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model"
written by Zutong Duan, Yansong Wang, Yanfeng Xing,
published by Journal of Transportation Technologies, Vol.5 No.2, 2015

has been cited by the following article(s):

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CrossRef

[1]	Objective evaluation model of sound quality
	Vehicle Interior Sound Quality, 2023

[2]	Oceanaut's Personal Acoustic Comfort Prediction Model and Sound Environment Improvement Method in the Cabin of a Deep-Sea Manned Submersible
	Ergonomics, 2022

[3]	Prediction of Heart Disease with Principal Component Analysis Feature Selection and Machine Learning Classifiers
	2021

[4]	On the Development of an Acoustic-Driven Method to Improve Driver's Comfort Based on Deep Reinforcement Learning
	2020

[5]	A weight optimized artificial neural network for automated software test oracle
	2020

[6]	Sound Quality Prediction of Fuel Cell Vehicles Based on Regression Analysis
	2020

[7]	Mechanical Faults Diagnosis of High-Voltage Circuit Breaker via Hybrid Features and Integrated Extreme Learning Machine
	2019

[8]	“Quiet down!”: A study on psychoacoustic annoyance within vehicles
	2018

[9]	다중회귀분석 및 인공신경망을 이용한 과부하 굴삭기 소음의 음질 인덱스
	2018

[10]	Psychoacoustic Bias in Vehicle Interior Noise–Preliminary Study
	Procedia Engineering, 2017

[11]	„Application of Artificial Neural Networks for Understanding the Quality and Masculinity Perception of Electric Shavers “
	2017

[12]	Estimation of Electric Shaver Sound Quality Using Artificial Neural Networks
	2016

[13]	Improving Heart Disease Data Classification Using Group Search Optimization
	2016

[14]	An Optimized Feature Selection Method for E-Learning Recommender System Using Deep Neural Network based on Multilayer Perceptron

[1]	Car Door Sound Quality Assessment - A Review for NVH Performance Research International Journal of Engineering and Advanced Technology, 2024 DOI:10.35940/ijeat.C4357.13030224

[2]	Oceanaut’s personal acoustic comfort prediction model and sound environment improvement method in the cabin of a Deep-Sea manned submersible Ergonomics, 2023 DOI:10.1080/00140139.2022.2152110

[3]	Oceanaut’s personal acoustic comfort prediction model and sound environment improvement method in the cabin of a Deep-Sea manned submersible Ergonomics, 2023 DOI:10.1080/00140139.2022.2152110

[4]	Oceanaut’s Personal Acoustic Comfort Prediction Model and Sound Environment Improvement Method in the Cabin of a Deep-Sea Manned Submersible Ergonomics, 2022 DOI:10.1080/00140139.2022.2152110

[5]	On the Development of an Acoustic-Driven Method to Improve Driver’s Comfort Based on Deep Reinforcement Learning IEEE Transactions on Intelligent Transportation Systems, 2021 DOI:10.1109/TITS.2020.2977983

[6]	Sound Quality Prediction of Fuel Cell Vehicles Based on Regression Analysis Journal of Physics: Conference Series, 2020 DOI:10.1088/1742-6596/1605/1/012062

[7]	A weight optimized artificial neural network for automated software test oracle Soft Computing, 2020 DOI:10.1007/s00500-020-05197-9

[8]	Mechanical Faults Diagnosis of High-Voltage Circuit Breaker via Hybrid Features and Integrated Extreme Learning Machine IEEE Access, 2019 DOI:10.1109/ACCESS.2019.2915252

[9]	Psychoacoustic metrics for assessing the quality of automotive HMIs’ impulsive sounds Applied Acoustics, 2018 DOI:10.1016/j.apacoust.2018.03.007

[10]	Sound Quality Index of Excavator's Noise Under Overload Operation Using Multiple Regression Analysis and Artificial Neural Network Transaction of the Korean Society of Automotive Engineers, 2018 DOI:10.7467/KSAE.2018.26.6.819

[11]	Psychoacoustic Bias in Vehicle Interior Noise – Preliminary Study Procedia Engineering, 2017 DOI:10.1016/j.proeng.2017.03.052

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