| 支持向量机的水声信号分类识别 |
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| 引用本文： | 王红萍,吕琳,陈晓辉,金彦丰.支持向量机的水声信号分类识别[J].舰船电子工程,2008,28(12). |
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| 作者姓名： | 王红萍 吕琳 陈晓辉 金彦丰 |
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| 作者单位： | 91388部队,湛江,524022 |
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| 摘 要： | 研究水声信号识别特征的提取,在此基础上采用支持向量机理论,提出了一种水声信号的分类识别算法.该算法选取两类水声信号,提取它们的混沌特征值关联维数和h2熵作为目标信息,每类信号各提取32组数据,取两类水声信号各8组数据作为训练样本,训练支持向量机,其它样本用于验证.结果表明,支持向量机的分类算法能实现对目标的有效分类,分类效果较好,比较适合小样本、非线性分类.
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| 关 键 词： | 水声信号 混沌特征值 支持向量机 分类识别 |
Classification and Recognition of Underwater Acoustic Signals Based on Support Vector Machine |
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| Wang Hongping,Lv Lin,Chen Xiaohui,Jin Yanfeng.Classification and Recognition of Underwater Acoustic Signals Based on Support Vector Machine[J].Ship Electronic Engineering,2008,28(12). |
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| Authors: | Wang Hongping Lv Lin Chen Xiaohui Jin Yanfeng |
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| Institution: | 91388部队,湛江,524022 |
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| Abstract: | The feature extraction of underwater acoustic signals is primary studied.According to the chaotic theory,a detection and target recognition algorithm of underwater acoustic signals is presented.Two classes of underwater acoustic signals are selected to calculate their correlation dimension and h2 entropy.Then these chaotic characteristic parameters are used to train SVM and verify the classification effect.The results show that SVM is effectual for the classification of underwater acoustic signals,It also s... |
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| Keywords: | underwater acoustic signal chaotic characteristic parameter support vector machine classification and recognition |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
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