核极限学习机及其在线序贯变体在数据预测中的实验研究与MATLAB实现,核极限学习机与在线序贯核极限学习方法在数据预测中的应用：实验结果比对与对比分析,基于核极限学习机KELM、在线顺序极限学习机OS
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核极限学习机及其在线序贯变体在数据预测中的实验研究与MATLAB实现,核极限学习机与在线序贯核极限学习方法在数据预测中的应用：实验结果比对与对比分析,基于核极限学习机KELM、在线顺序极限学习机OS-ELM、在线贯序核极限学习机OSKELM、遗忘因子的在线贯列核极限学习F-OSKELM和自适应遗忘因子的在线贯列核极限学习AF-OSKELM数据预测，下图为5种方法在matlab平台上的实验结果。,核极限学习机KELM;在线顺序极限学习机OS-ELM;在线贯序核极限学习OSKELM;遗忘因子在线预测;自适应遗忘因子在线预测。,基于多版本核极限学习机算法的数据预测研究：MATLAB实验结果分析 <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/90426603/2/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/90426603/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">基于核极限学习机<span class="_ _0"> </span><span class="ff2">KELM<span class="_"> </span></span>的数据预测技术及其在<span class="_ _0"> </span><span class="ff2">Matl<span class="_ _1"></span>ab<span class="_ _0"> </span><span class="ff1">平台上的实验研究</span></span></div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">一、引言</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">随着大数据时<span class="_ _2"></span>代的到来，数<span class="_ _2"></span>据预测技术已<span class="_ _2"></span>成为众多领域<span class="_ _2"></span>研究的热点。<span class="_ _2"></span>核极限学习机<span class="_ _2"></span>（<span class="ff2">KELM</span>）</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">作为一种新兴的机器学习方法，<span class="_ _3"></span>在数据预测领域表现出强大的性能。<span class="_ _3"></span>本文将探讨基于核极限</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">学<span class="_ _2"></span>习<span class="_ _4"></span>机<span class="_ _5"> </span><span class="ff2">KELM<span class="_ _5"> </span></span>的<span class="_ _4"></span>五<span class="_ _2"></span>种<span class="_ _2"></span>不<span class="_ _4"></span>同<span class="_ _2"></span>方<span class="_ _4"></span>法<span class="_ _2"></span>：<span class="_ _4"></span>在<span class="_ _2"></span>线<span class="_ _4"></span>顺<span class="_ _2"></span>序<span class="_ _4"></span>极<span class="_ _2"></span>限<span class="_ _4"></span>学<span class="_ _2"></span>习<span class="_ _4"></span>机<span class="_ _5"> </span><span class="ff2">OS-ELM<span class="_ _2"></span></span>、<span class="_ _4"></span>在<span class="_ _2"></span>线<span class="_ _4"></span>贯<span class="_ _2"></span>序<span class="_ _4"></span>核<span class="_ _2"></span>极<span class="_ _4"></span>限<span class="_ _2"></span>学<span class="_ _4"></span>习<span class="_ _2"></span>机</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">OSKELM<span class="ff1">、<span class="_ _3"></span>遗忘因子的在线贯列核极限学习<span class="_ _0"> </span><span class="ff2">F-OSKELM<span class="_ _0"> </span></span>以及自适应遗忘因子的在线贯列核极</span></div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">限学习<span class="_ _0"> </span><span class="ff2">AF-OSKELM</span>，并在<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _0"> </span></span>平台上进行实验验证。</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">二、方法介绍</div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">1. <span class="_ _6"> </span><span class="ff1">核极限学习机<span class="_ _0"> </span></span>KELM</div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">KELM<span class="_"> </span><span class="ff1">是一种基于核方法的极限学习机算法，<span class="_ _7"></span>它通过引入核技巧，<span class="_ _7"></span>将输入空间中的线性不可</span></div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">分问题转化为高维特征空间中的线性可分问题，从而提高了算法的泛化能力。</div><div class="t m0 x1 h2 yc ff2 fs0 fc0 sc0 ls0 ws0">2. <span class="_ _6"> </span><span class="ff1">在线顺序极限学习机<span class="_ _0"> </span></span>OS-ELM</div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">OS-ELM<span class="_"> </span><span class="ff1">是一种在线学习算法，<span class="_ _8"></span>它可以在接收到新数据时实时更新模型，<span class="_ _8"></span>而不需要重新训练</span></div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">整个模型。这种算法具有较高的学习效率和实时性。</div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">3. <span class="_ _6"> </span><span class="ff1">在线贯序核极限学习机<span class="_ _0"> </span></span>OSKELM</div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">OSKELM<span class="_ _6"> </span><span class="ff1">是<span class="_ _9"> </span></span>KELM<span class="_ _9"> </span><span class="ff1">与<span class="_ _6"> </span></span>OS-ELM<span class="_ _9"> </span><span class="ff1">的结合，<span class="_ _a"></span>它利用了<span class="_ _9"> </span><span class="ff2">KELM<span class="_ _6"> </span></span>的核技巧和<span class="_ _9"> </span><span class="ff2">OS-ELM<span class="_ _6"> </span></span>的在线学习能力，</span></div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">可以在线处理大规模数据，并保持较高的预测精度。</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">4. <span class="_ _6"> </span><span class="ff1">遗忘因子的在线贯列核极限学习<span class="_ _0"> </span></span>F-OSKELM</div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">F-OSKELM<span class="_"> </span><span class="ff1">引入了遗忘因子，<span class="_ _b"></span>用于在在线学习中处理过时数据。<span class="_ _c"></span>通过引入遗忘因子，<span class="_ _b"></span>算法可</span></div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">以更好地适应数据的动态变化，提高预测的准确性。</div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">5. <span class="_ _6"> </span><span class="ff1">自适应遗忘因子的在线贯列核极限学习<span class="_ _0"> </span></span>AF-OSKELM</div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">AF-OSKELM<span class="_"> </span><span class="ff1">是<span class="_ _6"> </span></span>F-OSKELM<span class="_"> </span><span class="ff1">的改进版本，<span class="_ _7"></span>它采用自适应调整遗忘因子的策略，<span class="_ _7"></span>使算法能够根</span></div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">据数据的实际情况自动调整遗忘因子的值，进一步提高预测的准确性和适应性。</div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">三、<span class="ff2">Matlab<span class="_ _6"> </span></span>平台上的实验结果</div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">在<span class="_ _0"> </span><span class="ff2">Matlab<span class="_"> </span></span>平台上，我们对这五种方法进行了实验验证。实验结果<span class="_ _2"></span>表明，这五种方法在数据</div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">预测方面均表现出良好的性能。其中，<span class="ff2">AF-OSKELM<span class="_"> </span></span>由于采用自适应调整遗忘因子的策略，</div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">使得算法在处理动态变化的数据时表现出更高的准确性和适应性。而<span class="_ _0"> </span><span class="ff2">F-OSKELM</span>、<span class="ff2">OSKELM</span></div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">和<span class="_ _0"> </span><span class="ff2">KELM<span class="_"> </span></span>也表现出较好的预测性能。<span class="_ _7"></span>相比之下，<span class="_ _7"></span><span class="ff2">OS-ELM<span class="_ _6"> </span><span class="ff1">由于缺乏核技巧的支持，<span class="_ _7"></span>在处理复</span></span></div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">杂数据时可能存在一定的局限性。</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>