基于DEGWO-BP算法优化的数据回归预测系统-一种混合灰狼与差分进化算法的神经网络参数调整方法,DEGWO-BP基于差分改进灰狼算法优化BP神经网络的数据回归预测 Matlab语言程序已调试好
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基于DEGWO-BP算法优化的数据回归预测系统——一种混合灰狼与差分进化算法的神经网络参数调整方法,DEGWO-BP基于差分改进灰狼算法优化BP神经网络的数据回归预测 Matlab语言程序已调试好，无需更改代码直接替Excel运行你先用，你就是创新多变量单输出，回归预测也可以前加好友成分类或时间序列单列预测，回归效果如图1所示～差分改进灰狼算法DEGWO优化的参数为：BP神经网络的初始权重与偏置。灰狼优化算法的改进点为：针对差分进化易陷入局部最优和灰狼算法易早熟停滞的缺点，利用差分进化的变异、选择算子维持种群的多样性，然后引入灰狼算法与差分进化的交叉、选择算子进行全局搜索。在整个寻优过程中，反复迭代渐进收敛。注：1.附赠测试数据，数据格式如图2所示～2.注释清晰，适合新手小白运行main文件一键出图～3.仅包含Matlab代码，后可保证原始程序运行4.模型只是提供一个衡量数据集精度的方法，因此无法保证替数据就一定得到您满意的结果～,DEGWO-BP; 差分改进灰狼算法; BP神经网络; 数据回归预测; Matlab语言; 程序调试; Excel运行; 多变量单输出 <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/90341212/2/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/90341212/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">**DEGWO-BP<span class="_ _0"> </span><span class="ff2">混合算法在数据回归预测中的应用</span>**</div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">在当今的大数据时代<span class="ff3">，</span>数据回归预测是许多领域中不可或缺的环节<span class="ff4">。</span>为了更精确地预测多变量单输出</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">数据<span class="ff3">，</span>本文将探讨一种基于差分改进灰狼算法<span class="ff3">（<span class="ff1">DEGWO</span>）</span>优化<span class="_ _1"> </span><span class="ff1">BP<span class="_ _0"> </span></span>神经网络的方法<span class="ff3">，</span>并利用<span class="_ _1"> </span><span class="ff1">Matlab</span></div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">语言进行实现<span class="ff4">。</span></div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">一<span class="ff4">、</span>算法背景及理论依据</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">BP<span class="ff3">（</span>Back Propagation<span class="ff3">）<span class="ff2">神经网络是一种广泛使用的神经网络模型</span>，<span class="ff2">但在某些复杂或高维数据中</span></span></div><div class="t m0 x1 h2 y7 ff3 fs0 fc0 sc0 ls0 ws0">，<span class="ff2">其初始权重与偏置的选择可能影响网络的收敛速度和预测精度<span class="ff4">。</span>因此</span>，<span class="ff2">优化<span class="_ _1"> </span><span class="ff1">BP<span class="_ _0"> </span></span>神经网络的参数显</span></div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">得尤为重要<span class="ff4">。</span>差分进化算法和灰狼算法是两种常用的优化算法<span class="ff3">，</span>但它们各自存在一些不足<span class="ff3">：</span>差分进化</div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">易陷入局部最优<span class="ff3">，</span>而灰狼算法易早熟停滞<span class="ff4">。</span>针对这些不足<span class="ff3">，</span>我们提出了差分改进灰狼算法<span class="ff3">（<span class="ff1">DEGWO</span>）</span></div><div class="t m0 x1 h2 ya ff3 fs0 fc0 sc0 ls0 ws0">，<span class="ff2">旨在综合两种算法的优点<span class="ff4">。</span></span></div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">二<span class="ff4">、<span class="ff1">DEGWO<span class="_ _0"> </span></span></span>算法的改进点</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">DEGWO<span class="_ _0"> </span><span class="ff2">算法针对差分进化的变异<span class="ff4">、</span>选择算子进行改进<span class="ff3">，</span>以维持种群的多样性<span class="ff4">。</span>同时<span class="ff3">，</span>引入灰狼算法的</span></div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">全局搜索能力<span class="ff3">，</span>通过与差分进化的交叉<span class="ff4">、</span>选择算子相结合<span class="ff3">，</span>形成一种新的混合优化策略<span class="ff4">。</span>在整个寻优</div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">过程中<span class="ff3">，</span>算法反复迭代渐进收敛<span class="ff3">，</span>从而优化<span class="_ _1"> </span><span class="ff1">BP<span class="_ _0"> </span></span>神经网络的初始权重与偏置<span class="ff4">。</span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">三<span class="ff4">、<span class="ff1">Matlab<span class="_ _0"> </span></span></span>程序实现及使用说明</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff2">程序已调试好<span class="ff3">，</span>无需更改代码<span class="ff4">。</span>只需将测试数据替换原有的<span class="_ _1"> </span></span>Excel<span class="_ _0"> </span><span class="ff2">文件即可运行<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff2">数据格式应遵循图<span class="_ _1"> </span></span>2<span class="_ _0"> </span><span class="ff2">所示的规范<span class="ff3">，</span>确保程序正确读取数据<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff2">程序注释清晰<span class="ff3">，</span>适合新手小白运行<span class="ff4">。</span>只需打开<span class="_ _1"> </span></span>main<span class="_ _0"> </span><span class="ff2">文件<span class="ff3">，</span>一键即可出图<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">4.<span class="_ _2"> </span><span class="ff2">程序支持售前加好友功能<span class="ff3">，</span>用户可灵活选择分类或时间序列单列预测模式<span class="ff4">。</span>当用于回归预测时<span class="ff3">，</span></span></div><div class="t m0 x2 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">其效果如图<span class="_ _1"> </span><span class="ff1">1<span class="_ _0"> </span></span>所示<span class="ff4">。</span></div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">四<span class="ff4">、</span>应用场景及效果</div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">该算法在多变量单输出数据的回归预测中表现出色<span class="ff4">。</span>通过<span class="_ _1"> </span><span class="ff1">DEGWO<span class="_ _0"> </span></span>算法优化的<span class="_ _1"> </span><span class="ff1">BP<span class="_ _0"> </span></span>神经网络能够更快地</div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">收敛<span class="ff3">，</span>并提高预测精度<span class="ff4">。</span>无论是用于售前分类预测还是时间序列单列预测<span class="ff3">，</span>该算法都能提供有效的支</div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">持<span class="ff4">。</span></div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">五<span class="ff4">、</span>结论</div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>