CNN卷积神经网络回归预测算法实现（基于Matlab 2018b及以上版本）-代码附样本供实验，Excel数据可替换使用,CNN卷积神经网络回归预测算法实践与Matlab应用-适用于Matlab
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CNN卷积神经网络回归预测算法实现（基于Matlab 2018b及以上版本）——代码附样本供实验，Excel数据可替换使用,CNN卷积神经网络回归预测算法实践与Matlab应用——适用于Matlab 2018b以上版本,CNN 卷积神经网络回归预测算法（基于Matlab实现）特殊要求：Matlab版本应高于2018bMATLAB代码，多输入单输出，结果如图数据直接用，附样本供实验。代码运行无误，直接更Excel数据即可实现。不负责详解，拿完直接发邮箱。,CNN; 卷积神经网络; 回归预测算法; Matlab 2018b以上版本; 代码运行无误; Excel数据; 样本供实验; 邮件发送。,基于Matlab 2018b以上版本的CNN卷积神经网络回归预测算法代码实现 <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/90425732/2/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/90425732/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">探索<span class="_ _0"> </span></span>CNN<span class="_ _0"> </span><span class="ff2">在回归预测算法中的应用</span>——<span class="ff2">基于<span class="_ _0"> </span></span>Matlab<span class="_ _0"> </span><span class="ff2">的实现</span>**</div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">摘要：</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">本文将探讨如何利用卷积神经网络（<span class="ff1">CNN</span>）进行回归预测算法的实现。我们将以<span class="_ _0"> </span><span class="ff1">Matlab<span class="_ _0"> </span></span>作</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">为开发平台，通过一个多输入单输出的实例来展示<span class="_ _0"> </span><span class="ff1">CNN<span class="_ _0"> </span></span>在处理回归问题时的效果。<span class="_ _1"></span>本文将</div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">不涉及详细的算法解释，<span class="_ _2"></span>而是直接提供可运行的代码和样本数据，<span class="_ _2"></span>使读者能够快速上手并应</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">用到实际项目中。</div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">一、引言</div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">随着<span class="_ _3"></span>人工<span class="_ _3"></span>智能<span class="_ _3"></span>技术<span class="_ _3"></span>的发<span class="_ _3"></span>展，卷<span class="_ _3"></span>积神<span class="_ _3"></span>经网<span class="_ _3"></span>络（<span class="_ _3"></span><span class="ff1">CNN</span>）<span class="_ _3"></span>在图<span class="_ _3"></span>像识<span class="_ _3"></span>别、<span class="_ _3"></span>语音<span class="_ _3"></span>识别<span class="_ _3"></span>等领<span class="_ _3"></span>域取<span class="_ _3"></span>得了<span class="_ _3"></span>显</div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">著的成功<span class="_ _3"></span>。除了分类<span class="_ _3"></span>问题，<span class="ff1">CNN<span class="_"> </span></span>在回归预<span class="_ _3"></span>测问题中也<span class="_ _3"></span>展现出了<span class="_ _3"></span>强大的能<span class="_ _3"></span>力。本文将<span class="_ _3"></span>通过一</div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">个具体实例，展示如何利用<span class="_ _0"> </span><span class="ff1">Matlab<span class="_ _0"> </span></span>实现一个基于<span class="_ _0"> </span><span class="ff1">CNN<span class="_ _0"> </span></span>的回归预测算法。</div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">二、方法与实现</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">1. <span class="_ _4"> </span><span class="ff2">数据准备</span></div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">为了演示<span class="_ _0"> </span><span class="ff1">CNN<span class="_ _4"> </span></span>在回归预测中的效果，我们使用一组样本数据。这些数据可以是任何形式的</div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">输入输出对，例如时间序列数据、<span class="_ _1"></span>传感器数据等。<span class="_ _1"></span>数据应被整理成<span class="_ _4"> </span><span class="ff1">Excel<span class="_"> </span></span>格式，<span class="_ _1"></span>并准备好用</div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">于训练和测试。</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">2. Matlab<span class="_ _4"> </span><span class="ff2">环境配置</span></div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">确保您的<span class="_ _0"> </span><span class="ff1">Matlab<span class="_ _4"> </span></span>版本高于<span class="_ _0"> </span><span class="ff1">2018b</span>，以便使用最新的<span class="_ _0"> </span><span class="ff1">CNN<span class="_"> </span></span>功能和优化。</div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">3. <span class="_ _4"> </span><span class="ff2">构建<span class="_ _0"> </span></span>CNN<span class="_ _4"> </span><span class="ff2">模型</span></div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">在<span class="_ _0"> </span><span class="ff1">Matlab<span class="_ _4"> </span></span>中，我们可以使用<span class="_ _0"> </span><span class="ff1">Deep Learning Toolbox<span class="_"> </span></span>来构建和训练<span class="_ _4"> </span><span class="ff1">CNN<span class="_"> </span></span>模型。<span class="_ _1"></span>以下是一个</div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">简单的<span class="_ _0"> </span><span class="ff1">CNN<span class="_ _4"> </span></span>模型结构示例：</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="_ _4"> </span><span class="ff2">定义输入层和卷积层</span></div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">layers = [</div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0"> <span class="_ _5"> </span>imageInputLayer([inputSize inputSize 1]) % <span class="_ _4"> </span><span class="ff2">假设输入为灰度图像，需根据实际情况调整</span></div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">尺寸</div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0"> <span class="_ _5"> </span>convolution2dLayer(5,5) % <span class="_ _4"> </span><span class="ff2">卷积核大小设为<span class="_ _0"> </span></span>5x5<span class="ff2">，可调整</span></div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0"> <span class="_ _5"> </span>... % <span class="_ _4"> </span><span class="ff2">可以添加更多层和参数</span></div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">];</div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">```</div><div class="t m0 x1 h2 y1e ff2 fs0 fc0 sc0 ls0 ws0">注意<span class="_ _2"></span>：<span class="_ _2"></span>这里只是一个模型结构的框架，您需要根据自己的问题和数据来调整层数、每层的参</div><div class="t m0 x1 h2 y1f ff2 fs0 fc0 sc0 ls0 ws0">数等。</div><div class="t m0 x1 h2 y20 ff1 fs0 fc0 sc0 ls0 ws0">4. <span class="_ _4"> </span><span class="ff2">训练模型</span></div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>