Sound quality prediction for power coupling mechanism of HEV based on CEEMD-HT and RVM

The sound quality evaluation model based on complementary ensemble empirical mode decomposition (CEEMD), Hilbert transform (HT) and relevance vector machine (RVM) is proposed to predict the sound quality of acoustical signals at power coupling mechanism of a hybrid electric vehicle (HEV). The techni...

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Vydané v:	Neural computing & applications Ročník 33; číslo 14; s. 8201 - 8216
Hlavní autori:	Lu, Yi, Zuo, Yanyan, Wang, Hui, Wu, Chuangang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.07.2021 Springer Nature B.V
Predmet:	Accuracy Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Coupling Data Mining and Knowledge Discovery Electric vehicles Energy value Experiments Frequencies Hilbert transformation Hybrid electric vehicles Image Processing and Computer Vision Machine learning Mathematical models Model accuracy Neural networks Noise Parameters Probability and Statistics in Computer Science Quality assessment S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications Signal processing Signal quality Sound Special Issue on Intelligent Computing Methodologies in Machine learning for IoT Applications Support vector machines Wavelet transforms Hilbert transform Sound quality Complementary ensemble empirical mode decomposition Relevance vector machine
ISSN:	0941-0643, 1433-3058
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