基于天牛须优化算法BAS优化SVM参数c和g的多输入单输出拟合预测建模（Matlab程序，含详细注释与多种结果图输出）,天牛须优化BAS支持向量机SVM参数c和g的多输入单输出预测建模工具，Matla
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基于天牛须优化算法BAS优化SVM参数c和g的多输入单输出拟合预测建模（Matlab程序，含详细注释与多种结果图输出）,天牛须优化BAS支持向量机SVM参数c和g的多输入单输出预测建模工具，Matlab程序高效制图与评价指标展示,天牛须优化算法BAS优化支持向量机SVM的c和g参数做多输入单输出的拟合预测建模。程序内注释详细直接替数据就可以使用。程序语言为matlab。程序直接运行可以出拟合预测图，迭代优化图，线性拟合预测图，多个预测评价指标。想要的加好友我吧。PS:以下效果图为测试数据的效果图，主要目的是为了显示程序运行可以出的结果图，具体预测效果以个人的具体数据为准。2.由于每个人的数据都是独一无二的，因此无法做到可以任何人的数据直接替就可以得到自己满意的效果。3.拿后程序后可以无偿帮忙替数据，无原理讲解。4.只拿后单个程序的不议价，两个及以上可接受议价。,天牛须优化算法;BAS优化;支持向量机SVM;c和g参数;多输入单输出;拟合预测建模;Matlab程序;注释详细;直接运行;拟合预测图;迭代优化图;线性拟合预测图;预测评价指标,基于天牛须算法优化SVM参数的M <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/90425903/2/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/90425903/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">技术博客文章：天牛须优化算法与<span class="_ _0"> </span><span class="ff2">SVM<span class="_"> </span></span>拟合预测建模的探索</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">在数据分析和机器学习领域，<span class="_ _1"></span>优化算法和模型的选择对于预测结果的准确性至关重要。<span class="_ _1"></span>本文</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">将围绕<span class="_ _2"></span>天牛须<span class="_ _2"></span>优化算<span class="_ _2"></span>法（<span class="ff2">BAS<span class="_ _2"></span></span>）和<span class="_ _0"> </span><span class="ff2">SVM<span class="_"> </span></span>的<span class="_ _0"> </span><span class="ff2">c<span class="_"> </span></span>和<span class="_ _0"> </span><span class="ff2">g<span class="_"> </span></span>参数做多<span class="_ _2"></span>输入单<span class="_ _2"></span>输出的<span class="_ _2"></span>拟合预<span class="_ _2"></span>测建模<span class="_ _2"></span>进行</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">探讨。<span class="_ _2"></span>我们<span class="_ _2"></span>将通过<span class="_ _2"></span>一个<span class="_ _2"></span>实际案<span class="_ _2"></span>例，<span class="_ _2"></span>展示如<span class="_ _2"></span>何使<span class="_ _2"></span>用<span class="_ _0"> </span><span class="ff2">MATLAB<span class="_"> </span></span>实现这<span class="_ _2"></span>一建模<span class="_ _2"></span>过程<span class="_ _2"></span>，并获<span class="_ _2"></span>得预<span class="_ _2"></span>测</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">结果和迭代优化图。</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">一、引言</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">在当<span class="_ _2"></span>今的<span class="_ _2"></span>大数<span class="_ _2"></span>据时<span class="_ _2"></span>代，<span class="_ _2"></span>如何<span class="_ _2"></span>从海<span class="_ _2"></span>量数<span class="_ _2"></span>据<span class="_ _2"></span>中提<span class="_ _2"></span>取有<span class="_ _2"></span>价值<span class="_ _2"></span>的信<span class="_ _2"></span>息，<span class="_ _2"></span>成为<span class="_ _2"></span>了一<span class="_ _2"></span>个重<span class="_ _2"></span>要的<span class="_ _2"></span>研究<span class="_ _2"></span>课题<span class="_ _2"></span>。</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">支持向量机<span class="_ _3"></span>（<span class="ff2">SVM</span>）<span class="_ _3"></span>作为一种常用的机器学习算法，<span class="_ _3"></span>在分类和回归问题中都有着广泛的应用。</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">而天牛<span class="_ _2"></span>须优化<span class="_ _2"></span>算法（<span class="_ _2"></span><span class="ff2">BAS</span>）作<span class="_ _2"></span>为一<span class="_ _2"></span>种新兴<span class="_ _2"></span>的优化<span class="_ _2"></span>方法，<span class="_ _2"></span>可以有<span class="_ _2"></span>效地对<span class="_ _4"> </span><span class="ff2">SVM<span class="_"> </span></span>的参数进<span class="_ _2"></span>行优化<span class="_ _2"></span>，</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">提高模型的预测精度。</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">二、天牛须优化算法（<span class="ff2">BAS</span>）简介</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">天牛须优化算法是一种基于生物启发式的优化算法，<span class="_ _5"></span>通过模拟天牛觅食行为中的摆动和选择</div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">过程，<span class="_ _6"></span>实现对目标函数的优化。<span class="_ _6"></span>该算法具有简单易实现、<span class="_ _6"></span>搜索能力强等特点，<span class="_ _6"></span>可以有效地解</div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">决复杂优化问题。</div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">三、<span class="ff2">SVM<span class="_"> </span></span>的<span class="_ _0"> </span><span class="ff2">c<span class="_ _7"> </span></span>和<span class="_ _0"> </span><span class="ff2">g<span class="_"> </span></span>参数及多输入单输出拟合预测建模</div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">SVM<span class="_"> </span><span class="ff1">是一种基于<span class="_ _2"></span>统计学<span class="_ _2"></span>习理论的<span class="_ _2"></span>机器学<span class="_ _2"></span>习算法<span class="_ _2"></span>，通过<span class="_ _2"></span>在特征<span class="_ _2"></span>空间中找<span class="_ _2"></span>到一个<span class="_ _2"></span>最优分<span class="_ _2"></span>离超平</span></div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">面，实现<span class="_ _2"></span>对数据<span class="_ _2"></span>的分类或<span class="_ _2"></span>回归。<span class="_ _2"></span>其中，<span class="ff2">c<span class="_"> </span></span>和<span class="_ _0"> </span><span class="ff2">g<span class="_"> </span></span>是<span class="_ _0"> </span><span class="ff2">SVM<span class="_"> </span></span>的两个重要<span class="_ _2"></span>参数，分<span class="_ _2"></span>别代表<span class="_ _2"></span>惩罚因子</div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">和核函数的宽度参数。<span class="_ _1"></span>在多输入单输出的拟合预测建模中，<span class="_ _1"></span>我们需要根据具体问题选择合适</div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">的<span class="_ _0"> </span><span class="ff2">c<span class="_ _0"> </span></span>和<span class="_ _0"> </span><span class="ff2">g<span class="_ _0"> </span></span>参数，以获得最佳的预测效果。</div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">四、<span class="ff2">MATLAB<span class="_ _0"> </span></span>实现及程序注释</div><div class="t m0 x1 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">下面是一个使用<span class="_ _0"> </span><span class="ff2">MATLAB<span class="_ _0"> </span></span>实现的<span class="_ _0"> </span><span class="ff2">SVM<span class="_"> </span></span>拟合预测建模的示例程序，<span class="_ _8"></span>其中包含了天牛须优化算</div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">法对<span class="_ _0"> </span><span class="ff2">c<span class="_ _0"> </span></span>和<span class="_ _0"> </span><span class="ff2">g<span class="_ _0"> </span></span>参数的优化过程。<span class="_ _1"></span>程序中的注释直接替换数据即可使用，<span class="_ _9"></span>运行后可以得到拟合预</div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">测图、迭代优化图、线性拟合预测图以及多个预测评价指标。</div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _7"> </span><span class="ff1">加载数据或替换为具体数据集</span></div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">% data = ...; % <span class="_ _7"> </span><span class="ff1">这里为假设的待加载或已定义的数据</span></div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _7"> </span><span class="ff1">使用<span class="_ _0"> </span></span>BAS<span class="_ _0"> </span><span class="ff1">算法对<span class="_ _0"> </span></span>c<span class="_ _0"> </span><span class="ff1">和<span class="_ _0"> </span></span>g<span class="_ _0"> </span><span class="ff1">参数进行优化</span></div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0">options = optimoptions('bayesopt', 'UseParallel', true); % <span class="_ _7"> </span><span class="ff1">设置优化选项</span></div><div class="t m0 x1 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0">[c_best, <span class="_ _a"> </span>g_best] <span class="_ _a"> </span>= <span class="_ _a"> </span>bayesopt(@objective_function, <span class="_ _a"> </span>c_min:c_max, <span class="_ _a"> </span>g_min:g_max, <span class="_ _a"> </span>'Options', </div><div class="t m0 x1 h2 y1e ff2 fs0 fc0 sc0 ls0 ws0">options); % <span class="_ _7"> </span><span class="ff1">调用<span class="_ _0"> </span></span>BAS<span class="_ _0"> </span><span class="ff1">算法进行参数优化</span></div><div class="t m0 x1 h2 y1f ff2 fs0 fc0 sc0 ls0 ws0">% objective_function<span class="_ _7"> </span><span class="ff1">为定义好的目标函数，根据具体问题实现</span></div><div class="t m0 x1 h2 y20 ff2 fs0 fc0 sc0 ls0 ws0">% c_min:c_max<span class="_ _0"> </span><span class="ff1">和<span class="_ _0"> </span></span>g_min:g_max<span class="_"> </span><span class="ff1">为参数<span class="_ _7"> </span></span>c<span class="_"> </span><span class="ff1">和<span class="_ _7"> </span></span>g<span class="_"> </span><span class="ff1">的搜索范围</span></div><div class="t m0 x1 h2 y21 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _7"> </span><span class="ff1">使用最优参数<span class="_ _0"> </span></span>c_best<span class="_ _7"> </span><span class="ff1">和<span class="_ _0"> </span></span>g_best<span class="_"> </span><span class="ff1">训练<span class="_ _7"> </span></span>SVM<span class="_"> </span><span class="ff1">模型</span></div><div class="t m0 x1 h2 y22 ff2 fs0 fc0 sc0 ls0 ws0">SVMModel <span class="_ _b"></span>= <span class="_ _b"></span>fitcsvm(data, <span class="_ _b"></span>labels, <span class="_ _b"></span>'KernelFunction', <span class="_ _b"></span>'RBF', <span class="_ _b"></span>'KernelScale', <span class="_ _b"></span>g_best, <span class="_ _b"></span>'BoxConstraint', </div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>