基于混沌-高斯变异-麻雀搜索算法（CGSSA）优化BP神经网络（CGSSA-BP）的回归预测（含优化前后对比）MATLAB代码 代码注释清楚 main为主程序，可以读取EXCEL数据 很方便
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基于混沌-高斯变异-麻雀搜索算法（CGSSA）优化BP神经网络（CGSSA-BP）的回归预测（含优化前后对比）MATLAB代码 代码注释清楚。main为主程序，可以读取EXCEL数据。很方便，容易上手。（电厂运行数据为例） <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/90185046/2/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/90185046/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">优化算法在神经网络的应用中起着重要的作用<span class="ff2">，</span>可以提高预测模型的准确性和泛化能力<span class="ff3">。</span>本文将介绍</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">一种基于混沌<span class="ff4">-</span>高斯变异<span class="ff4">-</span>麻雀搜索算法<span class="ff2">（<span class="ff4">CGSSA</span>）</span>优化<span class="_ _0"> </span><span class="ff4">BP<span class="_ _1"> </span></span>神经网络<span class="ff2">（<span class="ff4">CGSSA-BP</span>）</span>的回归预测方法</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">，<span class="ff1">并通过<span class="_ _0"> </span><span class="ff4">MATLAB<span class="_ _1"> </span></span>代码给出具体实现<span class="ff3">。</span></span></div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">首先<span class="ff2">，</span>我们需要了解<span class="_ _0"> </span><span class="ff4">BP<span class="_ _1"> </span></span>神经网络和优化算法的原理<span class="ff3">。<span class="ff4">BP<span class="_ _1"> </span></span></span>神经网络是一种常用的人工神经网络模型<span class="ff2">，</span></div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">通过训练集的反向传播算法来更新网络的权值和偏置<span class="ff2">，</span>从而实现对输入数据的预测<span class="ff3">。</span>然而<span class="ff2">，</span>传统的<span class="_ _0"> </span><span class="ff4">BP</span></div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">神经网络容易陷入局部最优解<span class="ff2">，</span>导致预测效果不佳<span class="ff3">。</span>因此<span class="ff2">，</span>引入优化算法对<span class="_ _0"> </span><span class="ff4">BP<span class="_ _1"> </span></span>神经网络进行改进是</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">必要的<span class="ff3">。</span></div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">本文提出的<span class="_ _0"> </span><span class="ff4">CGSSA<span class="_ _1"> </span></span>是一种混合算法<span class="ff2">，</span>结合了混沌算法<span class="ff3">、</span>高斯变异和麻雀搜索算法的优点<span class="ff3">。</span>混沌算法通</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">过引入混沌序列来增加搜索的随机性和多样性<span class="ff2">，</span>从而避免陷入局部最优解<span class="ff3">。</span>高斯变异则通过变异操作</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">对个体进行扰动<span class="ff2">，</span>增加搜索的范围和多样性<span class="ff3">。</span>麻雀搜索算法则模拟了麻雀寻找食物的行为<span class="ff2">，</span>通过觅食</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">和觅食场景选择来优化问题<span class="ff3">。</span></div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">在本文的回归预测中<span class="ff2">，</span>我们以电厂运行数据为例进行实验<span class="ff3">。</span>首先<span class="ff2">，</span>我们编写了主程序<span class="_ _0"> </span><span class="ff4">main<span class="ff2">，</span></span>可以读</div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">取<span class="_ _0"> </span><span class="ff4">EXCEL<span class="_ _1"> </span></span>数据<span class="ff2">，</span>方便用户使用和上手<span class="ff3">。</span>接下来<span class="ff2">，</span>我们使用<span class="_ _0"> </span><span class="ff4">CGSSA<span class="_ _1"> </span></span>优化<span class="_ _0"> </span><span class="ff4">BP<span class="_ _1"> </span></span>神经网络<span class="ff2">，</span>通过训练集对</div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">网络进行训练<span class="ff2">，</span>得到优化后的模型<span class="ff3">。</span></div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">通过优化前后的对比实验<span class="ff2">，</span>我们可以发现<span class="ff2">，</span>使用<span class="_ _0"> </span><span class="ff4">CGSSA<span class="_ _1"> </span></span>优化的<span class="_ _0"> </span><span class="ff4">BP<span class="_ _1"> </span></span>神经网络在回归预测任务中取得了</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">更好的效果<span class="ff3">。</span>优化后的模型具有更高的预测准确性和更好的泛化能力<span class="ff2">，</span>能够更好地适应各种输入数据</div><div class="t m0 x1 h3 y11 ff3 fs0 fc0 sc0 ls0 ws0">。</div><div class="t m0 x1 h2 y12 ff4 fs0 fc0 sc0 ls0 ws0">MATLAB<span class="_ _1"> </span><span class="ff1">代码中的注释清晰明了<span class="ff2">，</span>方便读者理解和使用<span class="ff3">。</span>使用该代码可以快速实现<span class="_ _0"> </span></span>CGSSA-BP<span class="_ _1"> </span><span class="ff1">神经网</span></div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">络的回归预测任务<span class="ff2">，</span>并得到准确的结果<span class="ff3">。</span></div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">综上所述<span class="ff2">，</span>本文介绍了基于混沌<span class="ff4">-</span>高斯变异<span class="ff4">-</span>麻雀搜索算法优化<span class="_ _0"> </span><span class="ff4">BP<span class="_ _1"> </span></span>神经网络的回归预测方法<span class="ff2">，</span>并通过</div><div class="t m0 x1 h2 y15 ff4 fs0 fc0 sc0 ls0 ws0">MATLAB<span class="_ _1"> </span><span class="ff1">代码给出了具体实现<span class="ff3">。</span>实验结果表明<span class="ff2">，</span>优化后的模型在电厂运行数据预测中取得了较好的效</span></div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">果<span class="ff3">。</span>该方法为神经网络的优化提供了一种新的思路和实现方式<span class="ff2">，</span>对于其他回归预测任务也具有一定的</div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">参考价值<span class="ff3">。</span>希望本文能为读者提供一些有益的思考和借鉴<span class="ff2">，</span>促进技术的发展和应用<span class="ff3">。</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>