卷积神经网络在RadioML2016.10A数据集上的信号识别：基于ResNet的分类准确率与损失函数分析,基于ResNet的卷积神经网络在RadioML2016.10A数据集上的信号识别与性能分析

IiKvcZaWZIP卷积神经网络识别信号数据集种信号识别分类  1MB

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ZIP 卷积神经网络识别信号数据集种信号识别分类 大约有13个文件
  1. 1.jpg 106.08KB
  2. 2.jpg 23.99KB
  3. 3.jpg 226.69KB
  4. 卷积神经网络在信号识别中的应用.txt 1.99KB
  5. 卷积神经网络在信号识别中的应用一引言在无线.txt 1.76KB
  6. 卷积神经网络在信号识别中的应用以数据集.txt 2.11KB
  7. 卷积神经网络在信号识别中的应用以数据集为例.doc 2.17KB
  8. 卷积神经网络在信号识别中的应用以数据集为例.txt 2.28KB
  9. 卷积神经网络在信号识别中的应用以数据集为例一.txt 2.68KB
  10. 卷积神经网络识别信号数据集种信号识别分.html 504.96KB
  11. 文章标题利用卷积神经网络与.html 503.76KB
  12. 文章标题利用卷积神经网络与进行数据集的信号识别一引.txt 1.88KB
  13. 文章标题卷积神经网络在数据集上的信号.txt 1.86KB

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卷积神经网络在RadioML2016.10A数据集上的信号识别：基于ResNet的分类准确率与损失函数分析,基于ResNet的卷积神经网络在RadioML2016.10A数据集上的信号识别与性能分析——出图展示分类准确率、混淆矩阵及损失函数迭代曲线,卷积神经网络识别信号 ResNet RadioML2016.10A数据集11种信号识别分类 出图包含每隔2dB的分类准确率曲线、混淆矩阵、损失函数迭代曲线等 Python实现 ,卷积神经网络; ResNet; 信号识别; RadioML2016.10A数据集; 分类准确率曲线; 混淆矩阵; 损失函数迭代曲线; Python实现,卷积神经网络在RadioML2016数据集上的信号识别研究
<link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90405003/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90405003/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">卷积神经网络在信号识别中的应用</span>——<span class="ff2">以<span class="_ _0"> </span></span>RadioML2016.10A<span class="_ _1"> </span><span class="ff2">数据集为例</span>**</div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">一<span class="ff3">、</span>引言</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">随着无线通信技术的快速发展<span class="ff4">，</span>信号识别在通信系统中扮演着越来越重要的角色<span class="ff3">。</span>卷积神经网络<span class="ff4">（</span></div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">Convolutional Neural Networks<span class="ff4">，</span>CNN<span class="ff4">）<span class="ff2">作为一种深度学习模型</span>，<span class="ff2">在图像处理<span class="ff3">、</span>语音识别等领</span></span></div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">域取得了显著的成果<span class="ff3">。</span>本文将探讨如何使用卷积神经网络<span class="ff4">，</span>特别是<span class="_ _0"> </span><span class="ff1">ResNet<span class="_ _1"> </span></span>模型<span class="ff4">，</span>对</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">RadioML2016.10A<span class="_ _1"> </span><span class="ff2">数据集中的信号进行识别<span class="ff4">，</span>并使用<span class="_ _0"> </span></span>Python<span class="_ _1"> </span><span class="ff2">进行实现<span class="ff3">。</span></span></div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">二<span class="ff3">、<span class="ff1">ResNet<span class="_ _1"> </span></span></span>模型概述</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">ResNet<span class="ff4">（</span>Residual Network<span class="ff4">）<span class="ff2">是一种深度卷积神经网络</span>，<span class="ff2">其独特的残差学习结构可以有效解决深</span></span></div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">度神经网络中的梯度消失和退化问题<span class="ff3">。<span class="ff1">ResNet<span class="_ _1"> </span></span></span>通过引入残差模块<span class="ff4">，</span>使得网络能够学习输入和输出之</div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">间的残差<span class="ff4">，</span>从而更容易地优化深层网络的性能<span class="ff3">。</span></div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">三<span class="ff3">、<span class="ff1">RadioML2016.10A<span class="_ _1"> </span></span></span>数据集</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">RadioML2016.10A<span class="_ _1"> </span><span class="ff2">是一个公开的无线信号分类数据集<span class="ff4">，</span>包含了多种无线通信信号<span class="ff3">。</span>该数据集提供了</span></div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">信号的时域和频域特征<span class="ff4">，</span>为信号识别提供了丰富的信息<span class="ff3">。</span>我们将使用该数据集来训练和测试我们的卷</div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">积神经网络模型<span class="ff3">。</span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">四<span class="ff3">、</span>模型构建与训练</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff2">数据预处理<span class="ff4">：</span>对<span class="_ _0"> </span></span>RadioML2016.10A<span class="_ _1"> </span><span class="ff2">数据集中的信号进行预处理<span class="ff4">，</span>包括归一化<span class="ff3">、</span>分割等操作<span class="ff4">，</span></span></div><div class="t m0 x2 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">以便于模型的训练<span class="ff3">。</span></div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff2">构建模型<span class="ff4">：</span>使用<span class="_ _0"> </span></span>Python<span class="_ _1"> </span><span class="ff2">的深度学习框架<span class="ff4">（</span>如<span class="_ _0"> </span></span>TensorFlow<span class="_ _1"> </span><span class="ff2">或<span class="_ _0"> </span></span>PyTorch<span class="ff4">）<span class="ff2">构建<span class="_ _0"> </span></span></span>ResNet<span class="_ _1"> </span><span class="ff2">模型</span></div><div class="t m0 x2 h2 y13 ff3 fs0 fc0 sc0 ls0 ws0">。<span class="ff2">根据数据集的特点<span class="ff4">，</span>可以调整模型的层数</span>、<span class="ff2">滤波器数量等参数</span>。</div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff2">训练模型<span class="ff4">：</span>使用预处理后的数据集对模型进行训练<span class="ff4">，</span>通过反向传播算法更新模型的参数<span class="ff3">。</span>在训练</span></div><div class="t m0 x2 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">过程中<span class="ff4">，</span>可以使用损失函数<span class="ff4">（</span>如交叉熵损失函数<span class="ff4">）</span>来衡量模型的预测结果与真实结果之间的差距</div><div class="t m0 x2 h3 y16 ff3 fs0 fc0 sc0 ls0 ws0">。</div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">4.<span class="_ _2"> </span><span class="ff2">评估模型<span class="ff4">：</span>在验证集上评估模型的性能<span class="ff4">，</span>包括分类准确率<span class="ff3">、</span>混淆矩阵等指标<span class="ff3">。</span>同时<span class="ff4">，</span>可以绘制出</span></div><div class="t m0 x2 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">每隔<span class="_ _0"> </span><span class="ff1">2dB<span class="_ _1"> </span></span>的分类准确率曲线<span class="ff4">，</span>以便于观察模型在不同信噪比下的性能<span class="ff3">。</span></div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">五<span class="ff3">、</span>出图展示</div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff2">分类准确率曲线<span class="ff4">：</span>绘制出每隔<span class="_ _0"> </span></span>2dB<span class="_ _1"> </span><span class="ff2">的分类准确率曲线<span class="ff4">，</span>以便于观察模型在不同信噪比下的性能</span></div><div class="t m0 x2 h3 y1b ff3 fs0 fc0 sc0 ls0 ws0">。</div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff2">混淆矩阵<span class="ff4">：</span>绘制混淆矩阵<span class="ff4">，</span>以直观地展示模型在各个类别上的性能<span class="ff3">。</span></span></div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff2">损失函数迭代曲线<span class="ff4">：</span>绘制损失函数迭代曲线<span class="ff4">，</span>以便于观察模型在训练过程中的损失变化情况<span class="ff3">。</span></span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>
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