| 吴涢晖,邹士亚,庞新良,陈晓雷.应用支持向量机和人工神经网络对大气次声信号 识别的初步实验[J].,2020,39(2):216-222 |
| 应用支持向量机和人工神经网络对大气次声信号 识别的初步实验 |
| Experimental study on atmospheric infrasound signal recognition using SVM and ANN |
| 投稿时间：2019-07-26 修订日期：2020-02-28 |
| 中文摘要: |
| 针对STA/LTA算法从次声台站监测数据中提取的信号仍然包含噪声的问题，对支持向量机和神经网络的机器学习方法进行了研究。采用小波包分解的方法对信号进行重构，提取出各频带内的重构信号能量特征，对事件信号和噪声进行了识别实验，并分析了提高识别能力的方法，为工程应用提供理论参考。实验结果表明，在训练数据集不大的情况下，通过优化模型结构可以将两种方法的识别能力提高到可以接受的水平。 |
| 英文摘要: |
| Aiming at the problem that the signal extracted from infrasound station monitoring data by STA/LTA algorithm still contained noise, we made preliminary experimental studies on the machine learning method of support vector machine and neural network. We used a method of wavelet packet decomposition to reconstruct the signals, and extracted the energy characteristics from them. We also analyzed the methods to improve the recognition ability. The experimental results showed that the recognition ability of the two methods can be improved to an acceptable level by optimizing the model structure such as the training data set was small. |
| DOI：10.11684/j.issn.1000-310X.2020.02.006 |
| 中文关键词: 次声信号检测,小波包分解,神经网络,支持向量机 |
| 英文关键词: Infrasound signal detection, Wavelet packet decomposition, Neural network, Support vector machine |
| 基金项目: |
|
| 摘要点击次数: 1349 |
| 全文下载次数: 1199 |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
