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
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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>