基于Matlab的LSTM与BiLSTM一维信号二分类（含多分类）源程序：数据替换简易教程与详细注释，构建训练测试流程，准确率评估与混淆矩阵绘制 ,Matlab教程：LSTM与BiLSTM对一维信号二
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基于Matlab的LSTM与BiLSTM一维信号二分类（含多分类）源程序：数据替换简易教程与详细注释，构建训练测试流程，准确率评估与混淆矩阵绘制。,Matlab教程：LSTM与BiLSTM对一维信号二分类及多分类处理源程序（含数据） - 从数据加载到混淆矩阵绘制全流程详解,Matlab使用LSTM或BiLSTM对一维信号(语音信号、心电信号等)进行二分类源程序。也可以改成多分类。包含数据和代码，数据可以直接替为自己的数据。如果用BiLSTM，程序中只需要把lstmlayer改为bilstmlayer即为BiLSTM网络，其他地方不需要任何改动。注释详细，包教会和运行，后续有问题会积极解答。工作如下：1、加载数据集，一共为400个样本(正常200异常200)；选80％做为训练集(共320样本，160正常160异常)，剩余20%作为测试集(共80样本，40正常40异常)。2、构建LSTM网络，层数为两层。3、构建优化器options。4、训练。5、测试，计算准确率，绘制混淆矩阵。注：考虑到Matlab用LSTM分类一维信号的教程较少，此程序只用做帮助学习如何调整输入数 <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/90429726/2/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/90429726/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">**Matlab<span class="_ _0"> </span><span class="ff2">使用<span class="_ _0"> </span></span>LSTM<span class="_ _0"> </span><span class="ff2">或<span class="_ _0"> </span></span>BiLSTM<span class="_ _0"> </span><span class="ff2">一维信号二分类源程序</span>**</div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">一、背景介绍</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">近期<span class="_ _1"></span>，随<span class="_ _1"></span>着深<span class="_ _1"></span>度学<span class="_ _1"></span>习技<span class="_ _1"></span>术的<span class="_ _1"></span>不断<span class="_ _1"></span>进步<span class="_ _1"></span>，<span class="ff1">LSTM<span class="_ _1"></span></span>（长<span class="_ _1"></span>短期<span class="_ _1"></span>记忆<span class="_ _1"></span>）和<span class="_ _2"> </span><span class="ff1">BiLSTM</span>（<span class="_ _1"></span>双向<span class="_ _1"></span>长短<span class="_ _1"></span>时记<span class="_ _1"></span>忆）</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">在语音<span class="_ _1"></span>信号处理、心<span class="_ _1"></span>电信号分析等<span class="_ _1"></span>领域的应用越<span class="_ _1"></span>来越广泛。本<span class="_ _1"></span>篇技术博客将<span class="_ _1"></span>围绕如何使<span class="_ _1"></span>用</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">Matlab<span class="_"> </span><span class="ff2">编写一个二<span class="_ _1"></span>分类程序，<span class="_ _1"></span>该程序能够<span class="_ _1"></span>处理一维信号<span class="_ _1"></span>数据，尤其<span class="_ _1"></span>是语音信号<span class="_ _1"></span>和心电信号</span></div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">等。代码将直接给出，并附上数据替换说明。</div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">二、数据和代码准备</div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">为了<span class="_ _1"></span>展示此技<span class="_ _1"></span>术实现，<span class="_ _1"></span>假设你<span class="_ _1"></span>已经拥有<span class="_ _1"></span>了<span class="_ _0"> </span><span class="ff1">400<span class="_"> </span></span>个样本<span class="_ _1"></span>的数据集<span class="_ _1"></span>，这些样<span class="_ _1"></span>本均为<span class="_ _1"></span>一维信号<span class="_ _1"></span>。</div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">在数据准备过程中，<span class="_ _3"></span>请按照实际样本量和异常情况比例自行调整训练集和测试集的样本分配。</div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">其中正常数据<span class="_ _0"> </span><span class="ff1">200<span class="_ _0"> </span></span>例，异常数据<span class="_ _0"> </span><span class="ff1">200<span class="_ _0"> </span></span>例的数据已在实际环境中准备好。</div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">在编写程序之前，确保你的<span class="_ _0"> </span><span class="ff1">Matlab<span class="_"> </span></span>环境中安装了相关依赖包和模<span class="_ _1"></span>块。代码实现中将无需引</div><div class="t m0 x1 h2 yc ff2 fs0 fc0 sc0 ls0 ws0">入其他虚假参考资料或依赖，所有的功能和代码直接来自这里给出的范例。</div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">三、步骤详解</div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">以下是对如何使用<span class="_ _0"> </span><span class="ff1">Matlab<span class="_ _0"> </span></span>编写这个二分类程序的一些步骤说明：</div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">步骤一：加载数据集</div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">首先，<span class="_ _4"></span>你需要加载已准备好的数据集。<span class="_ _4"></span>这里可以直接使用<span class="_ _0"> </span><span class="ff1">Matlab<span class="_ _0"> </span></span>内置的<span class="ff1">`loaddata`</span>函数进行</div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">加载，并将加载的数据赋值给相应变量。假设你的数据加载文件名为<span class="ff1">`dataset.mat`</span>。</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">步骤二：构建<span class="_ _0"> </span><span class="ff1">LSTM<span class="_ _0"> </span></span>网络</div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">接下来，<span class="_ _1"></span>你可以使用<span class="_ _0"> </span><span class="ff1">Matlab<span class="_"> </span></span>的神经网络工<span class="_ _1"></span>具箱构建一个基<span class="_ _1"></span>于<span class="_ _0"> </span><span class="ff1">LSTM<span class="_ _0"> </span></span>的二分类网络<span class="_ _1"></span>。你可以</div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">使用<span class="_ _0"> </span><span class="ff1">Matlab<span class="_ _0"> </span></span>内置的神经网络模块来完成这个步骤。下面是一个示例代码框架：</div><div class="t m0 x1 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">% <span class="_ _5"> </span><span class="ff2">载入数据集，这里是示例数据加载，实际应用时需要替换为你的实际加载代码</span></div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">% load_data(Xtrain,Ytrain); % <span class="_ _5"> </span><span class="ff2">这里是加载数据部分，替换为你自己的代码</span></div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">% <span class="_ _5"> </span><span class="ff2">设置<span class="_ _0"> </span></span>LSTM<span class="_ _0"> </span><span class="ff2">网络层数和隐藏层单元数（根据你的实际需求来设定）</span></div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">layerCount = 2; % <span class="_ _5"> </span><span class="ff2">两层<span class="_ _5"> </span></span>L<span class="_ _1"></span>STM<span class="_ _5"> </span><span class="ff2">网络层数</span></div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">numUnits = 100; % LSTM<span class="_"> </span><span class="ff2">隐藏层单元数（可以根据你的数据调整）</span></div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">% <span class="_ _5"> </span><span class="ff2">构建<span class="_ _2"> </span></span>LSTM<span class="_"> </span><span class="ff2">网络结构，<span class="_ _1"></span>这里假设<span class="_ _1"></span>使用<span class="_ _0"> </span></span>lstmlayer<span class="_"> </span><span class="ff2">作为层<span class="_ _1"></span>类名，具体<span class="_ _1"></span>实现需要<span class="_ _1"></span>根据实际<span class="_ _1"></span>情况</span></div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0">调整</div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">net = lstmLayer(layerCount, numUnits); % <span class="_ _5"> </span><span class="ff2">使用<span class="_ _0"> </span></span>BiLSTM<span class="_ _0"> </span><span class="ff2">时，<span class="_ _6"></span>需要改为<span class="_ _0"> </span><span class="ff1">bilstmlayer<span class="_ _5"> </span></span>作为层类名</span></div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>