基于人工智能的噪声源识别关键技术研究

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中文摘要：为提升噪声源识别的准确性和实时性,研究卷积神经网络(Convolutional Neural Network,CNN)、支持向量机(Support Vector Machine,SVM)、长短期记忆(Long Short-Term Memory,LSTM)网络在噪声源识别中的应用.通过实验验证,不同模型在不同类型噪声上的表现各有优劣,其中CNN综合表现最佳.结果表明,这些人工智能技术的应用能够有效促进噪声治理,为智慧环保提供技术支撑.

外文标题：Research on Key Technologies for Noise Source Identification Based on Artificial Intelligence

外文摘要：This study explores the use of Convolutional Neural Network(CNN),Support Vector Machine(SVM),and Long Short-Term Memory(LSTM)networks for improving the accuracy and real-time performance of noise source identification.The experimental results show that different models have their own advantages and disadvantages in different types of noise,among which CNN has the best comprehensive performance.The results show that the application of these artificial intelligence technologies can effectively promote noise control and provide technical support for intelligent environmental protection..

外文关键词：

noise source identificationConvolutional Neural Networks(CNN)Support Vector Machines(SVM)Long Short-Term Memory(LSTM)

作者：

周海林

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作者单位：

广西制造工程职业技术学院,广西南宁 530100

关键词：

噪声源识别卷积神经网络(CNN) 支持向量机(SVM) 长短期记忆(LSTM)

出版年：

2024

DOI：