基于改进的鲸鱼优化算法GSWOA的Elman参数优化及多输入单输出拟合预测模型建立-Matlab实现,基于改进的鲸鱼优化算法的Elman参数优化和多输入单输出拟合预测模型（MATLAB实现）,基于改
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基于改进的鲸鱼优化算法GSWOA的Elman参数优化及多输入单输出拟合预测模型建立——Matlab实现,基于改进的鲸鱼优化算法的Elman参数优化和多输入单输出拟合预测模型（MATLAB实现）,基于改进的鲸鱼优化算法GSWOA对Elman的参数进行优化，建立多输入单输出的拟合预测模型。程序内注释详细直接替数据可用。程序语言为matlab。,GSWOA优化算法; Elman参数优化; 多输入单输出预测模型; MATLAB程序; 程序内注释详细,MATLAB中的GSWOA算法优化Elman网络参数——多输入单输出预测模型 <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/90425802/2/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/90425802/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">基于<span class="_ _0"></span>改进<span class="_ _0"></span>的鲸<span class="_ _0"></span>鱼优<span class="_ _0"></span>化算<span class="_ _0"></span>法<span class="_ _1"> </span><span class="ff2">GSWOA<span class="_"> </span></span>对<span class="_ _1"> </span><span class="ff2">Elman<span class="_ _1"> </span></span>神经网<span class="_ _0"></span>络的<span class="_ _0"></span>参数<span class="_ _0"></span>优化<span class="_ _0"></span>及多<span class="_ _0"></span>输入<span class="_ _0"></span>单输<span class="_ _0"></span>出拟<span class="_ _0"></span>合预<span class="_ _0"></span>测</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">模型</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">基于改进的鲸鱼优化算法<span class="_ _1"> </span></span>GSWOA<span class="_ _2"> </span><span class="ff1">对<span class="_ _1"> </span></span>Elman<span class="_ _2"> </span><span class="ff1">神经网络进行参数优化</span></div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">导入或准备数据</span></div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">假设我们有一组多输入单输出的数据集，</span>data<span class="_ _1"> </span><span class="ff1">中包含输入和输出</span></div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">% data = [input_data; output_data];</div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">初始化<span class="_ _1"> </span></span>Elman<span class="_ _2"> </span><span class="ff1">神经网络参数</span></div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">初始化网络结构，如隐藏层神经元数量等</span></div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">hiddenLayerSize = 10; % <span class="_ _2"> </span><span class="ff1">隐藏层神经元数量</span></div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">初始化<span class="_ _1"> </span></span>Elman<span class="_ _2"> </span><span class="ff1">网络，并随机设置初始权重和偏置</span></div><div class="t m0 x1 h2 yc ff2 fs0 fc0 sc0 ls0 ws0">elmanNet = fitnet(hiddenLayerSize);</div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">elmanNet.divideParam.trainRatio = 0.7; % <span class="_ _2"> </span><span class="ff1">训练集比例</span></div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">elmanNet.divideParam.valRatio = 0.15; <span class="_ _3"> </span>% <span class="_ _2"> </span><span class="ff1">验证集比例</span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">elmanNet.divideParam.testRatio = 0.15; % <span class="_ _2"> </span><span class="ff1">测试集比例</span></div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">基于改进的鲸鱼优化算法<span class="_ _1"> </span></span>GSWOA<span class="_ _2"> </span><span class="ff1">进行参数优化</span></div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">定义目标函数，这里我们以网络的训练误差作为优化目标</span></div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">% functionValue = @(params)trainError(elmanNet,params,data);</div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">初始化<span class="_ _1"> </span></span>GSWOA<span class="_ _2"> </span><span class="ff1">参数</span></div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">鲸鱼群规模、迭代次数等</span></div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">gswoaParams = initializeGSWOAParams();</div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">进行优化循环</span></div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">for iter = 1:gswoaParams.maxIterations</div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>% <span class="_ _2"> </span><span class="ff1">根据<span class="_ _1"> </span></span>GSWOA<span class="_ _2"> </span><span class="ff1">的规则更新鲸鱼的位置和速度等参数</span></div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>gswoaUpdate(elmanNet, gswoaParams);</div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0"> </div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>% <span class="_ _2"> </span><span class="ff1">使用更新后的参数重新训练<span class="_ _1"> </span></span>Elman<span class="_ _2"> </span><span class="ff1">网络</span></div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>[elmanNet, ~] = train(elmanNet, data); % <span class="_ _2"> </span><span class="ff1">这里我们简化地用<span class="_ _2"> </span></span>train<span class="_"> </span><span class="ff1">函数表示训练过程</span></div><div class="t m0 x1 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0"> </div><div class="t m0 x1 h2 y1e ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>% <span class="_ _2"> </span><span class="ff1">计算当前网络在训练集上的误差</span></div><div class="t m0 x1 h2 y1f ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>currentTrainError = computeTrainError(elmanNet, data);</div><div class="t m0 x1 h2 y20 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _4"> </span>gswoaRecordTrainError(iter, currentTrainError); % <span class="_ _2"> </span><span class="ff1">记录误差以便后续分析</span></div><div class="t m0 x1 h2 y21 ff2 fs0 fc0 sc0 ls0 ws0">end</div><div class="t m0 x1 h2 y22 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">建立多输入单输出的拟合预测模型</span></div><div class="t m0 x1 h2 y23 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">使用优化后的<span class="_ _1"> </span></span>Elman<span class="_ _2"> </span><span class="ff1">网络进行预测</span></div><div class="t m0 x1 h2 y24 ff2 fs0 fc0 sc0 ls0 ws0">predictedOutput = predict(elmanNet, testData); % testData<span class="_ _2"> </span><span class="ff1">为测试集数据</span></div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>