基于贝叶斯优化参数的CNN-BiLSTM回归预测模型：多输入单输出架构与高代码质量,基于贝叶斯(bayes)优化卷积神经网络-双向长短期记忆网络(CNN-BiLSTM)回归预测，bayes-CNN-B
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基于贝叶斯优化参数的CNN-BiLSTM回归预测模型：多输入单输出架构与高代码质量,基于贝叶斯(bayes)优化卷积神经网络-双向长短期记忆网络(CNN-BiLSTM)回归预测，bayes-CNN-BiLSTM多输入单输出模型。优化参数为：学习率，隐含层节点，正则化参数。评价指标包括:R2、MAE、MSE、RMSE和MAPE等，代码质量极高，方便学习和替数据。运行环境matlab2020b及以上。,核心关键词：贝叶斯优化; 卷积神经网络; 双向长短期记忆网络; 回归预测; 多输入单输出模型; 优化参数; 学习率; 隐含层节点; 正则化参数; 评价指标; R2; MAE; MSE; RMSE; MAPE; 代码质量; 运行环境; matlab2020b。,基于贝叶斯优化的CNN-BiLSTM回归预测模型，多参数调控，高代码质量 <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/90341615/2/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/90341615/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">基于贝叶斯优化卷积神经网络与双向长短期记忆网络的回归预测模型</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">一<span class="ff2">、</span>引言</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">随着深度学习技术的不断发展<span class="ff3">，</span>卷积神经网络<span class="ff3">（<span class="ff4">CNN</span>）</span>和长短期记忆网络<span class="ff3">（<span class="ff4">LSTM</span>）</span>在各种复杂任务中</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">取得了显著的成果<span class="ff2">。</span>为了进一步提高预测的准确性和稳定性<span class="ff3">，</span>本文提出了一种基于贝叶斯优化的卷积</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">神经网络与双向长短期记忆网络<span class="ff3">（<span class="ff4">CNN-BiLSTM</span>）</span>的回归预测模型<span class="ff2">。</span>该模型采用多输入单输出的结构</div><div class="t m0 x1 h2 y6 ff3 fs0 fc0 sc0 ls0 ws0">，<span class="ff1">并针对学习率<span class="ff2">、</span>隐含层节点和正则化参数等关键参数进行贝叶斯优化<span class="ff2">。</span></span></div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">二<span class="ff2">、</span>模型架构</div><div class="t m0 x1 h2 y8 ff4 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _0"> </span>CNN<span class="_ _1"> </span><span class="ff1">层<span class="ff3">：</span></span>CNN<span class="_ _1"> </span><span class="ff1">层能够自动提取输入数据的局部特征<span class="ff3">，</span>降低数据的维度<span class="ff3">，</span>同时保留重要的信息<span class="ff2">。</span></span></div><div class="t m0 x2 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">通过卷积操作和池化操作<span class="ff3">，</span>提取出数据的高层次特征<span class="ff2">。</span></div><div class="t m0 x1 h2 ya ff4 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _0"> </span>BiLSTM<span class="_ _1"> </span><span class="ff1">层<span class="ff3">：</span></span>BiLSTM<span class="_ _1"> </span><span class="ff1">层是一种双向的长短期记忆网络<span class="ff3">，</span>能够处理序列数据的时间依赖性问题<span class="ff2">。</span></span></div><div class="t m0 x2 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">它能够捕捉到数据中的长期依赖关系<span class="ff3">，</span>并输出具有时间序列特性的特征<span class="ff2">。</span></div><div class="t m0 x1 h2 yc ff4 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _0"> </span><span class="ff1">贝叶斯优化<span class="ff3">：</span>针对学习率<span class="ff2">、</span>隐含层节点和正则化参数等关键参数进行贝叶斯优化<span class="ff3">，</span>以寻找最优的</span></div><div class="t m0 x2 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">模型参数<span class="ff3">，</span>提高模型的预测性能<span class="ff2">。</span></div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">三<span class="ff2">、</span>参数优化</div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">针对模型的关键参数<span class="ff3">，</span>采用贝叶斯优化方法进行参数寻优<span class="ff2">。</span>通过设计合理的目标函数和约束条件<span class="ff3">，</span>利</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">用贝叶斯优化算法在参数空间中寻找最优的参数组合<span class="ff2">。</span></div><div class="t m0 x1 h2 y11 ff4 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _0"> </span><span class="ff1">学习率<span class="ff3">：</span>学习率是模型训练过程中的重要参数<span class="ff3">，</span>它决定了模型在每一次迭代中的更新步长<span class="ff2">。</span>过大</span></div><div class="t m0 x2 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">的学习率可能导致模型无法收敛<span class="ff3">，</span>过小的学习率则可能导致模型训练速度过慢<span class="ff2">。</span>通过贝叶斯优化</div><div class="t m0 x2 h2 y13 ff3 fs0 fc0 sc0 ls0 ws0">，<span class="ff1">可以在合理的范围内找到最优的学习率<span class="ff2">。</span></span></div><div class="t m0 x1 h2 y14 ff4 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _0"> </span><span class="ff1">隐含层节点<span class="ff3">：</span>隐含层节点的数量决定了模型的复杂度<span class="ff2">。</span>过多的节点可能导致模型过拟合<span class="ff3">，</span>而过少</span></div><div class="t m0 x2 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">的节点则可能导致模型欠拟合<span class="ff2">。</span>通过贝叶斯优化<span class="ff3">，</span>可以在保证模型性能的同时<span class="ff3">，</span>降低模型的复杂</div><div class="t m0 x2 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">度<span class="ff2">。</span></div><div class="t m0 x1 h2 y17 ff4 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _0"> </span><span class="ff1">正则化参数<span class="ff3">：</span>正则化参数用于防止模型过拟合<span class="ff2">。</span>通过在目标函数中添加正则化项<span class="ff3">，</span>可以控制模型</span></div><div class="t m0 x2 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">的复杂度<span class="ff2">。</span>通过贝叶斯优化<span class="ff3">，</span>可以找到合适的正则化参数<span class="ff3">，</span>以平衡模型的复杂度和泛化能力<span class="ff2">。</span></div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">四<span class="ff2">、</span>评价指标</div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">模型的性能评价指标包括<span class="_ _2"> </span><span class="ff4">R2<span class="ff2">、</span>MAE<span class="ff2">、</span>MSE<span class="ff2">、</span>RMSE<span class="_ _1"> </span></span>和<span class="_ _2"> </span><span class="ff4">MAPE<span class="_ _1"> </span></span>等<span class="ff2">。</span>这些指标分别从不同角度评价了模型</div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">的预测性能<span class="ff3">，</span>包括模型的解释力度<span class="ff2">、</span>预测误差<span class="ff2">、</span>均方误差<span class="ff2">、</span>均方根误差和平均绝对百分比误差等<span class="ff2">。</span></div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">五<span class="ff2">、</span>代码实现与运行环境</div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>