BP神经网络预测模型：MATLAB代码实现，多输入单输出，附样本数据与结果图,BP神经网络预测：MATLAB代码实现多输入单输出，附样本数据与结果图，一键换数据即用 ,BP神经网络预测，MATLAB代

JwWgrkapdrZIP神经网络预测代码多输入单输出结  1.73MB

资源文件列表:

ZIP 神经网络预测代码多输入单输出结 大约有14个文件
  1. 1.jpg 117.46KB
  2. 2.jpg 124.99KB
  3. 3.jpg 53KB
  4. 4.jpg 101.36KB
  5. 在当前日益发展的信息时代中人工智能技术.docx 15.08KB
  6. 在计算机科学领域神经网络是一种常用的.docx 16.33KB
  7. 基于神经网络的预测模型代码与结果展示一.docx 52.31KB
  8. 探索神经网络预测.html 558.13KB
  9. 神经网络预测与代码分析随着科技的飞速发展人工智能技.docx 50.71KB
  10. 神经网络预测代码多输入单输.html 557.61KB
  11. 神经网络预测代码解析与实际应用一引言随着.html 558.35KB
  12. 神经网络预测技术分析以代码为例.docx 50.52KB
  13. 神经网络预测技术在代码解析随着数.docx 50.26KB
  14. 随着科技的不断发展和应用领域的扩.docx 49.57KB

资源介绍:

BP神经网络预测模型：MATLAB代码实现，多输入单输出，附样本数据与结果图,BP神经网络预测：MATLAB代码实现多输入单输出，附样本数据与结果图，一键换数据即用。,BP神经网络预测，MATLAB代码 多输入单输出，结果如图，数据直接用，附样本数据。 ,BP神经网络预测; MATLAB代码; 多输入单输出; 结果如图; 换数据直接用; 附样本数据,BP神经网络预测：MATLAB代码实现，多输入单输出结果可视化及样本数据附带
<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/90426531/2/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/90426531/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">基于<span class="_ _0"> </span></span>BP<span class="_ _0"> </span><span class="ff2">神经网络的预测模型：</span>MATLAB<span class="_ _0"> </span><span class="ff2">代码与结果展示</span>**</div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">一、引言</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">BP<span class="ff2">（</span>Back Propagation<span class="ff2">）<span class="_ _1"></span>神经网络是一种通过反向传播算法对网络进行训练的多层前馈网络，</span></div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">常用于预测和分类等任务。<span class="_ _2"></span>本篇文章将通过<span class="_ _0"> </span><span class="ff1">MATLAB<span class="_ _0"> </span></span>实现一个多输入单输出的<span class="_ _0"> </span><span class="ff1">BP<span class="_ _0"> </span></span>神经网络</div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">预测模型，<span class="_ _3"></span>并对某一数据集进行实例展示。<span class="_ _3"></span>此外，<span class="_ _3"></span>还提供了一套简单的代码以供用户快速更</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">换数据并直接使用。</div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">二、<span class="ff1">BP<span class="_ _0"> </span></span>神经网络模型构建</div><div class="t m0 x1 h2 y8 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 y9 ff2 fs0 fc0 sc0 ls0 ws0">在开始之前，<span class="_ _2"></span>我们需要准备一组多输入单输出的数据集。<span class="_ _5"></span>这组数据集将作为我们训练和测试</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">BP<span class="_ _4"> </span><span class="ff2">神经网络的依据。</span></div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">2. MATLAB<span class="_ _0"> </span><span class="ff2">代码实现</span></div><div class="t m0 x1 h2 yc ff2 fs0 fc0 sc0 ls0 ws0">下面是一段<span class="_ _0"> </span><span class="ff1">MATLAB<span class="_ _0"> </span></span>代码，用于构建<span class="_ _0"> </span><span class="ff1">BP<span class="_ _0"> </span></span>神经网络模型并进行预测。</div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">% <span class="_ _4"> </span><span class="ff2">加载或准备样本数据</span></div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">% <span class="_ _4"> </span><span class="ff2">假设<span class="_ _0"> </span></span>inputs<span class="_ _0"> </span><span class="ff2">为输入数据，</span>targets<span class="_ _4"> </span><span class="ff2">为对应的目标输出</span></div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">inputs = ...; % <span class="_ _4"> </span><span class="ff2">这里填入你的输入数据</span></div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">targets = ...; % <span class="_ _4"> </span><span class="ff2">这里填入你的目标输出数据</span></div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">% <span class="_ _4"> </span><span class="ff2">定义网络结构：输入层神经元数量、隐藏层神经元数量、输出层神经元数量</span></div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">inputNeurons = size(inputs, 2); % <span class="_ _4"> </span><span class="ff2">输入层神经元数量由输入数据的特征数决定</span></div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">hiddenNeurons = 10; % <span class="_ _4"> </span><span class="ff2">隐藏层神经元数量，根据实际情况调整</span></div><div class="t m0 x1 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">outputNeurons = 1; % <span class="_ _4"> </span><span class="ff2">输出层神经元数量为<span class="_ _4"> </span></span>1<span class="ff2">，因为是多输入单输出模型</span></div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">% <span class="_ _4"> </span><span class="ff2">创建<span class="_ _0"> </span></span>BP<span class="_ _4"> </span><span class="ff2">神经网络</span></div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">net <span class="_ _6"></span>= <span class="_ _6"></span>fitnet(hiddenNeurons); <span class="_ _6"></span>% <span class="_ _4"> </span><span class="ff2">使用<span class="_ _0"> </span></span>fitnet<span class="_"> </span><span class="ff2">函数创建网络，<span class="_ _7"></span><span class="ff1">hiddenNeurons<span class="_ _0"> </span><span class="ff2">为隐藏层神经元数</span></span></span></div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">net.divideParam.trainRatio = 0.7; % <span class="_ _4"> </span><span class="ff2">设置训练集比例</span></div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">net.divideParam.valRatio = 0.15; % <span class="_ _4"> </span><span class="ff2">设置验证集比例</span></div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">net.divideParam.testRatio = 0.15; % <span class="_ _4"> </span><span class="ff2">设置测试集比例</span></div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">% <span class="_ _4"> </span><span class="ff2">训练网络</span></div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">[net,tr] = train(net,inputs',targets'); % <span class="_ _4"> </span><span class="ff2">使用<span class="_ _4"> </span></span>inputs<span class="_"> </span><span class="ff2">和<span class="_ _4"> </span></span>targets<span class="_"> </span><span class="ff2">训练网络</span></div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">% <span class="_ _4"> </span><span class="ff2">测试网络并获取预测结果</span></div><div class="t m0 x1 h2 y1e ff1 fs0 fc0 sc0 ls0 ws0">predictions = net(inputs'); % <span class="_ _4"> </span><span class="ff2">对输入数据进行预测</span></div><div class="t m0 x1 h2 y1f ff1 fs0 fc0 sc0 ls0 ws0">performance = perform(net, targets', predictions); % <span class="_ _4"> </span><span class="ff2">计算性能指标如均方误差等</span></div><div class="t m0 x1 h2 y20 ff1 fs0 fc0 sc0 ls0 ws0">disp('<span class="ff2">训练结果</span>:');</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>
100+评论
captcha
    相关资源