全向运动模型与动态窗口DWA算法：动态避障策略在MATLAB中的实现与应用,全向运动模型，动态窗口DWA，动态避障，matlab,全向运动模型; 动态窗口DWA; 动态避障; MATLAB,基于全向

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ZIP 全向运动模型动态窗口动态避.zip 大约有10个文件
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  2. 以下是一篇基于您提供的主题的完整源码和数据.doc 2.15KB
  3. 全向运动模型与动态窗口在动态避.txt 1.6KB
  4. 全向运动模型与动态窗口在动态避障中的.txt 1.82KB
  5. 全向运动模型与动态窗口在动态避障中的应用研究.txt 1.85KB
  6. 全向运动模型动态窗口动态避障.html 10.19KB
  7. 全向运动模型动态窗口及动态避障.doc 1.74KB
  8. 全向运动模型动态窗口及动态避障在中.txt 1.81KB
  9. 全向运动模型动态窗口及动态避障的仿真研究一.txt 2.08KB
  10. 全向运动模型动态窗口及动态避障的实现一引言在机器.txt 1.68KB

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全向运动模型与动态窗口DWA算法：动态避障策略在MATLAB中的实现与应用,全向运动模型，动态窗口DWA，动态避障，matlab ,全向运动模型; 动态窗口DWA; 动态避障; MATLAB,基于全向运动模型的动态窗口DWA避障算法在MATLAB中的实现
<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/90341517/2/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/90341517/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">以下是一篇基于您提供的主题的<span class="_ _0"> </span><span class="ff2">Matlab<span class="_ _1"> </span></span>完整源码和数据集的示例文章<span class="ff3">，</span>以满足您的要求<span class="ff4">。</span></div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">**<span class="ff1">基于<span class="_ _0"> </span></span>MFO-TCN-BiGRU-Attention<span class="_ _1"> </span><span class="ff1">飞蛾扑火算法优化的多变量时间序列预测</span>**</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">一<span class="ff4">、</span>引言</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">多变量时间序列预测是机器学习领域中常见的问题<span class="ff3">，</span>它涉及到根据历史数据预测未来趋势<span class="ff4">。</span>本文将使</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">用<span class="_ _0"> </span><span class="ff2">Matlab 2023<span class="_ _1"> </span></span>版以上<span class="ff3">，</span>基于<span class="_ _0"> </span><span class="ff2">MFO-TCN<span class="ff3">（</span></span>时间卷积网络<span class="ff3">）</span>和<span class="_ _0"> </span><span class="ff2">BiGRU<span class="ff3">（</span></span>双向门控循环单元<span class="ff3">）</span>模型融</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">合注意力机制进行飞蛾扑火算法优化的时间序列预测<span class="ff3">，</span>包括数据的完整源码和数据集的处理<span class="ff4">。</span></div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">二<span class="ff4">、</span>模型架构</div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff1">飞蛾扑火算法优化<span class="ff3">：</span>我们采用飞蛾扑火算法来优化模型中的关键参数<span class="ff3">，</span>包括学习率<span class="ff4">、</span>神经元个数</span></div><div class="t m0 x2 h2 y9 ff4 fs0 fc0 sc0 ls0 ws0">、<span class="ff1">注意力机制的键值和正则化参数</span>。</div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span>MFO-TCN-BiGRU<span class="_ _1"> </span><span class="ff1">模型<span class="ff3">：</span>模型包含一个基于<span class="_ _0"> </span></span>MFO<span class="_ _1"> </span><span class="ff1">优化的时间卷积网络<span class="ff3">（</span></span>TCN<span class="ff3">）<span class="ff1">和一个双向门控</span></span></div><div class="t m0 x2 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">循环单元<span class="ff3">（<span class="ff2">BiGRU</span>）<span class="ff4">。</span></span>这两个部分结合在一起<span class="ff3">，</span>利用各自的优点<span class="ff3">，</span>用于处理时间序列数据<span class="ff4">。</span>此外</div><div class="t m0 x2 h2 yc ff3 fs0 fc0 sc0 ls0 ws0">，<span class="ff1">还加入了注意力机制</span>，<span class="ff1">以便在预测过程中对不同特征进行加权<span class="ff4">。</span></span></div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">三<span class="ff4">、</span>数据集与预处理</div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">本例中使用的数据集为<span class="_ _0"> </span><span class="ff2">data<span class="ff3">，</span></span>需要进行必要的预处理以适应模型的输入要求<span class="ff4">。</span>预处理步骤包括数据</div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">清洗<span class="ff4">、</span>特征提取<span class="ff4">、</span>归一化等<span class="ff4">。</span>在处理多变量时间序列时<span class="ff3">，</span>应考虑历史特征的影响<span class="ff3">，</span>以捕捉时间序列的</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">动态特性<span class="ff4">。</span></div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">四<span class="ff4">、<span class="ff2">Matlab<span class="_ _1"> </span></span></span>代码实现</div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">以下是主运行文件<span class="_ _0"> </span><span class="ff2">main.m<span class="_ _1"> </span></span>的代码框架<span class="ff3">：</span></div><div class="t m0 x1 h3 y13 ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">加载数据集<span class="_ _0"> </span></span>data</div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">预处理数据<span class="ff3">（</span>如归一化等<span class="ff3">）</span></span></div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">定义模型参数<span class="ff3">（</span>如学习率<span class="ff4">、</span>神经元个数等<span class="ff3">）</span></span></div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">初始化<span class="_ _0"> </span></span>MFO-TCN-BiGRU-Attention<span class="_ _1"> </span><span class="ff1">模型</span></div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">训练模型</span></div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">进行多变量时间序列预测</span></div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">计算<span class="_ _0"> </span></span>R2<span class="ff4">、</span>MSE<span class="ff4">、</span>MAE<span class="ff4">、</span>MAPE<span class="_ _1"> </span><span class="ff1">和<span class="_ _0"> </span></span>RMSE<span class="_ _1"> </span><span class="ff1">评价指标</span></div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">% <span class="ff1">输出评价结果到命令窗口</span></div><div class="t m0 x1 h3 y1c ff2 fs0 fc0 sc0 ls0 ws0">```</div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>
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