數(shù)據(jù)預(yù)處理的重要性 超參數(shù)搜索空間設(shè)計(jì) 模型訓(xùn)練穩(wěn)定性保障 優(yōu)化算法參數(shù)調(diào)節(jié) 計(jì)算資源合理分配 代碼模塊化設(shè)計(jì) 實(shí)驗(yàn)結(jié)果的嚴(yán)格驗(yàn)證 日志與異常處理 安全與隱私保護(hù) 項(xiàng)目部署與應(yīng)用 系統(tǒng)架構(gòu)設(shè)計(jì) 部署平臺(tái)與環(huán)境準(zhǔn)備 模型加載與優(yōu)化 實(shí)時(shí)數(shù)據(jù)流處理 可視化與用戶界面 GPU/TPU加速推理 系統(tǒng)監(jiān)控與自動(dòng)化管理 自動(dòng)化CI/CD管道 API服務(wù)與業(yè)務(wù)集成 前端展示與結(jié)果導(dǎo)出 20安全性與用戶隱私 20數(shù)據(jù)加密與權(quán)限控制 20故障恢復(fù)與系統(tǒng)備份 20模型更新與維護(hù) 20項(xiàng)目未來改進(jìn)方向 21多模態(tài)數(shù)據(jù)融合 21算法優(yōu)化與加速 21增強(qiáng)學(xué)習(xí)與自適應(yīng)機(jī)制 21 21大規(guī)模分布式訓(xùn)練 21自動(dòng)化數(shù)據(jù)標(biāo)注與增強(qiáng) 21應(yīng)用領(lǐng)域擴(kuò)展 用戶體驗(yàn)與交互優(yōu)化 2安全保障與隱私保護(hù)升級(jí) 22項(xiàng)目總結(jié)與結(jié)論 2程序設(shè)計(jì)思路和具體代碼實(shí)現(xiàn) 2第一階段：環(huán)境準(zhǔn)備 22清空環(huán)境變量 22關(guān)閉報(bào)警信息 23關(guān)閉開啟的圖窗 23清空變量 清空命令行 檢查環(huán)境所需的工具箱 23配置GPU加速 24導(dǎo)入必要的庫 24第二階段：數(shù)據(jù)準(zhǔn)備 24數(shù)據(jù)導(dǎo)入和導(dǎo)出功能，以便用戶管理數(shù)據(jù)集 數(shù)據(jù)處理功能(填補(bǔ)缺失值和異常值的檢測(cè)和處理功能) 數(shù)據(jù)分析(平滑異常數(shù)據(jù)、歸一化和標(biāo)準(zhǔn)化等) 26特征提取與序列創(chuàng)建 26劃分訓(xùn)練集和測(cè)試集 27參數(shù)設(shè)置 27第三階段：算法設(shè)計(jì)和模型構(gòu)建及訓(xùn)練 28第四階段：模型預(yù)測(cè)及性能評(píng)估 32評(píng)估模型在測(cè)試集上的性能(用訓(xùn)練好的模型進(jìn)行預(yù)測(cè)) 行更全面的評(píng)估) 設(shè)計(jì)繪制誤差熱圖 3設(shè)計(jì)繪制殘差圖 3設(shè)計(jì)繪制預(yù)測(cè)性能指標(biāo)柱狀圖 3第五階段：精美GUI界面 34精美GUI界面 34第六階段：防止過擬合及參數(shù)調(diào)整 防止過擬合 超參數(shù)調(diào)整 增加數(shù)據(jù)集 48優(yōu)化超參數(shù) 48完整代碼整合封裝 49算法(WOA)優(yōu)化卷積門控循環(huán)單元進(jìn)行數(shù)據(jù)分類預(yù)測(cè)的詳細(xì)項(xiàng)目實(shí)例項(xiàng)目背景介紹的特征自動(dòng)提取能力和建模能力，廣泛應(yīng)用于圖像識(shí)在眾多深度學(xué)習(xí)模型中，卷積神經(jīng)網(wǎng)絡(luò)(CNN)因其在空間特征提取方面的卓越下且難以保證全局最優(yōu)解。鯨魚優(yōu)化算法(WOA)作為一種新興的群體智能優(yōu)化算法，以其簡(jiǎn)單、高效、易實(shí)現(xiàn)的特點(diǎn)，成為優(yōu)化深度學(xué)習(xí)模型超參數(shù)的理想選擇。通過WOA對(duì)本項(xiàng)目旨在基于WOA優(yōu)化卷積門控循環(huán)單元(CNN-GRU)模型，構(gòu)建一個(gè)高效、準(zhǔn)確的分結(jié)合早停法和權(quán)重共享策略，有效縮短模型訓(xùn)練時(shí)間，同時(shí)保證優(yōu)化過程中的模型準(zhǔn)確性與穩(wěn)定性，適合大規(guī)模數(shù)據(jù)應(yīng)用。設(shè)計(jì)通用框架，支持圖像、時(shí)序、文本等多種類型數(shù)據(jù)輸入，具備良好的擴(kuò)展性和適應(yīng)性，適合多領(lǐng)域分類任務(wù)。利用WOA的獨(dú)特圍捕機(jī)制和隨機(jī)搜索策略，提高參數(shù)優(yōu)化的全局搜索能力，避免陷入局部最優(yōu)，保證模型調(diào)優(yōu)的效果和可靠性。實(shí)現(xiàn)從數(shù)據(jù)預(yù)處理、模型訓(xùn)練、超參數(shù)優(yōu)化到結(jié)果預(yù)測(cè)的全流程自動(dòng)化，提高項(xiàng)目整體效率和可操作性，減少人工干預(yù)。項(xiàng)目應(yīng)用領(lǐng)域利用CNN-GRU模型對(duì)圖像序列或視頻數(shù)據(jù)進(jìn)行分類和識(shí)別，適用于安防監(jiān)控、人臉識(shí)別、醫(yī)療影像診斷等領(lǐng)域，實(shí)現(xiàn)高效智能化處理。對(duì)金融市場(chǎng)、氣象數(shù)據(jù)、傳感器監(jiān)測(cè)等時(shí)序數(shù)據(jù)進(jìn)行準(zhǔn)確預(yù)測(cè)和分類，助力風(fēng)險(xiǎn)管理、災(zāi)害預(yù)警、設(shè)備故障診斷等應(yīng)用場(chǎng)景。應(yīng)用于文本分類、情感分析、語音識(shí)別等任務(wù)，充分發(fā)揮GRU對(duì)序列依賴建模能力，提升文本數(shù)據(jù)理解與處理效率。復(fù)制%數(shù)據(jù)準(zhǔn)備load('data.mat');%假設(shè)數(shù)據(jù)包含X(特征)和Y(標(biāo)簽)%數(shù)據(jù)歸一化%參數(shù)初始化maxIter=50;%最大迭代次數(shù)popSize=20;%群體大小(鯨魚數(shù)量)%定義模型超參數(shù)搜索空間paramBounds=struct('convFilterNum',[16,64],...'convFilterSize',[3,7],...'gruUnits',[32,128],...%初始化鯨魚群體位置(超參數(shù)集合)population=zeros(popSpopulation(i,1)=randi(paramBounds.convFilterpopulation(i,2)=randi(paramBounds.convFilterpopulation(i,3)=randi(paramBounds.gruUnipopulation(i,4)=paramBounds.learningRat(paramBounds.learningRate(2)-paramBounds.learn%適應(yīng)度函數(shù)定義(驗(yàn)證集準(zhǔn)確率)functionfitness=modelFitness(params,X,Y)convFilterNum=round(params(1));convFilterSize=rougruUnits=roconvolutionldLayer(convFilterSizegruLayer(gruUnits,'OutputMfullyConnectedLayer(numoptions=trainingOptions('adam',...'InitialLearnRate',lr,...'MaxEpochs',10,...'MiniBatchSize',64,...'Shuffle','every-epoch',...'Verbose',false,...%數(shù)據(jù)分割(80%訓(xùn)練，20%驗(yàn)證)idx=randperm(size(X,1));trainIdx=idx(1:round(0.8*levalIdx=idx(round(0.8*length(iYTrain=categorical(YYVal=categorical(Y(valIdx));net=trainNetwork(XTrain,YTrain,layer序列建模模塊(GRU):門控循環(huán)單元作為一種改實(shí)現(xiàn)對(duì)長(zhǎng)序列依賴的捕獲。該模塊緊跟CNN輸出，負(fù)責(zé)挖掘時(shí)序數(shù)據(jù)中的動(dòng)態(tài)變化和時(shí)間自動(dòng)調(diào)整CNN-GRU模型的卷積核數(shù)量、濾波器大小、GRU單元數(shù)以及學(xué)習(xí)率等超參數(shù)。該項(xiàng)目模型描述及代碼示例convLayer積層，核大小5,濾波器數(shù)量32,填充保證輸出長(zhǎng)度一致gruLayer=gruLayer(64,'Output數(shù)64,輸出最后時(shí)刻隱藏狀態(tài)，適合序列分類fcLayer=fullyConnectedLayer(numClasses);%全softmaxLayer=softmaxLayer;%softmax層，將輸出轉(zhuǎn)換為概率分布classificationLayer=classificationLayer;%分類層，計(jì)算交叉熵?fù)p失sequenceInputLayer(inputSize)%輸入層，接受序列數(shù)據(jù)convLayer%卷積層reluLayer%激活函數(shù)層poolLayer%池化層gruLayer%GRU層，建模序列時(shí)序classificationLayer%分類損失層鯨魚優(yōu)化算法(WOA)超參數(shù)搜索示例functionbestParams=WOA_Optimize(fitnessFunc,bounds,maxIter,dim=size(bounds,1);%參population=rand(popSize,dim).*(bounds(:,2)'-bounds(:,1)’)+bounds(:,1)’;%初始化鯨魚位置bestParams=zeros(1,dim);a=2-iter*(2/maxIter);%線性遞減系數(shù)fitness=fitnessFunc(population(i,:));%計(jì)算適應(yīng)度iffitness<bestFitnessbestFitness=bestParams=populatiifp<0.5ifabs(A)<1D=abs(C*bestParams-populationpopulation(i,:)=bestParams-A*D;randWhale=population(randi(poD=abs(C*randWhale-populatiopopulation(i,:)=randWhaledistanceToBest=abs(bestParamspopulation(i,:)=distanceToBest*exp(1i*2*pi*+bestParams;%螺旋更新population(i,:)=max(min(population(i,:),bounds(:,bounds(:,1)’);%邊界處理項(xiàng)目模型算法流程圖diff復(fù)制數(shù)據(jù)輸入項(xiàng)目應(yīng)該注意事項(xiàng)結(jié)合領(lǐng)域知識(shí)和實(shí)驗(yàn)經(jīng)驗(yàn)，動(dòng)態(tài)調(diào)整邊界，提高WOA搜索效率和精度。采用批量歸一化、Dropout等技術(shù)，防止過擬合和梯度WOA算法中關(guān)鍵參數(shù)如探索系數(shù)和收斂因子需合理調(diào)節(jié)。引入自適應(yīng)機(jī)制，根據(jù)迭代進(jìn)程果，客觀評(píng)估WOA優(yōu)化優(yōu)勢(shì)，避免過擬合和偶然性結(jié)果。項(xiàng)目部署與應(yīng)用優(yōu)先選擇支持GPU加速的服務(wù)器或云平臺(tái)(如AWS、Azure、阿里云),確保深度學(xué)習(xí)模型GPU加速。通過Docker容器化部署，實(shí)現(xiàn)環(huán)境一致性和快速遷移，簡(jiǎn)化系統(tǒng)維使用MATLAB深度學(xué)習(xí)框架將訓(xùn)練完成的WOA-CNN-GRU模型導(dǎo)出為可部署格式，結(jié)合實(shí)時(shí)數(shù)據(jù)流處理可視化與用戶界面構(gòu)建基于Web的交互式可視化界面，實(shí)現(xiàn)數(shù)據(jù)上傳、模型運(yùn)行狀態(tài)展示及結(jié)果分析。集成權(quán)限管理，滿足不同業(yè)務(wù)角色的數(shù)據(jù)訪問和操作GPU/TPU加速推理利用GPU并行計(jì)算能力加速深度模型推理，顯著縮短響應(yīng)時(shí)間。針對(duì)硬件環(huán)境可選配系統(tǒng)監(jiān)控與自動(dòng)化管理API服務(wù)與業(yè)務(wù)集成項(xiàng)目總結(jié)與結(jié)論本項(xiàng)目成功構(gòu)建并實(shí)現(xiàn)了基于鯨魚優(yōu)化算法(WOA)優(yōu)化卷積門控循環(huán)單元(CNN-GRU)拓展更多應(yīng)用場(chǎng)景。安全性和隱私保護(hù)機(jī)制的加強(qiáng)將為項(xiàng)目應(yīng)用于敏感領(lǐng)域程序設(shè)計(jì)思路和具體代碼實(shí)現(xiàn)clearvars;%清除所有變量，釋放內(nèi)存，確保變量不會(huì)影響后續(xù)程序運(yùn)行關(guān)閉報(bào)警信息復(fù)制warning('off','all');%關(guān)閉所有警告信息，避免程序運(yùn)行時(shí)彈出干擾信息關(guān)閉開啟的圖窗復(fù)制closeall;%關(guān)閉所有打開的圖形窗口，避免圖窗混亂影響觀察復(fù)制clear;%清除工作區(qū)中的所有變量，確保環(huán)境干凈清空命令行復(fù)制clc;%清空命令行窗口，方便查看當(dāng)前程序輸出信息檢查環(huán)境所需的工具箱復(fù)制toolboxes=ver;%獲取當(dāng)前安裝的所有工具箱信息toolboxNames={toolboxes.Name};%提取工具箱名稱集合requiredToolboxes={'DeepLearningToolbox','StatisticsandMachineLearningToolbox','ParallelComputingToolbox'};%需要用到的工具箱名稱if~any(contains(toolboxNames,requiredToolboxes{i}))error(['缺少必要工具箱：’,requiredToolboxes{i},’,請(qǐng)先安裝該工具箱’]);%未安裝提示錯(cuò)誤，強(qiáng)制用戶安裝復(fù)制gpuDeviceCount=gpuDeviceCount;%獲取系統(tǒng)中的GPU設(shè)備數(shù)量ifgpuDeviceCount>0disp('未檢測(cè)到GPU設(shè)備，將使用CPU計(jì)算’);%沒有GPU時(shí)提示并使用復(fù)制%無需額外導(dǎo)入，DeepLearningToolbox函數(shù)默認(rèn)可用復(fù)制dataTable=readtable('dataset.csv');%從CSV文件讀取數(shù)據(jù)，自動(dòng)識(shí)別列名和數(shù)據(jù)格式%將處理后的數(shù)據(jù)保存為MAT文件，便于快速加載save('processedData.mat','dataTable');%保存為MAT文件以便后續(xù)加載使用復(fù)制%假設(shè)數(shù)據(jù)中包含文本或時(shí)間序列數(shù)據(jù)，需要對(duì)時(shí)間序列進(jìn)行窗口化處理windowSize=30;%定義滑動(dòng)窗口長(zhǎng)度為30stepSize=1;%窗口滑動(dòng)步長(zhǎng)為1numSamples=size(dataArra%窗口化處理，生成三維數(shù)據(jù)[窗口大小，特征維度，窗口數(shù)量]numFeatures=size(danumWindows=floor((numSamples-windowSize)/stepSize)+1;%計(jì)算窗口dataWindows=zeros(windowSize,numFeatures窗口數(shù)組idxStart=(i-1)*stepSize+1;%窗口dataWindows(:,:,i)=dataArray(idxStart:idxEnd,:);%切片窗口數(shù)據(jù)存入三維數(shù)組數(shù)據(jù)處理功能(填補(bǔ)缺失值和異常值的檢測(cè)和處理功能)復(fù)制%檢測(cè)缺失值，使用線性插值填補(bǔ)缺失數(shù)據(jù)featData=dataArray(:,feat);%取單個(gè)特征列missingIdx=isnan(featData);%找出缺失值索引ifany(missingIdx)featData(missingIdx)=interp1(findfeatData(~missingIdx),find(missingIdx),'linear','extrap’);%dataArray(:,feat)=featData;%%異常值檢測(cè)，使用3倍標(biāo)準(zhǔn)差法剔除異常并用中位數(shù)填補(bǔ)%創(chuàng)建序列輸入格式：時(shí)間步長(zhǎng)×特征維度×樣本數(shù)(之前窗口化數(shù)據(jù)結(jié)構(gòu))%結(jié)合原始序列和平滑標(biāo)準(zhǔn)化特征，形成輸入序列sequenceData=zeros(windowSize,numFeatures,numWindows);%初始化序idxStart=(i-1)*stepSize+1;sequenceData(:,:,i)=stdData(idxStart:idxEnd,:);%%標(biāo)簽對(duì)應(yīng)生成(假設(shè)標(biāo)簽列為第一列)labels=table2arlabelSeq=labels(windowSize:end);trainRatio=0.8;%80%訓(xùn)練，20%numTrain=floor(trainRatio*numWindows);%訓(xùn)練樣本數(shù)XTrain=sequenceData(:,:,1:numTrain);%訓(xùn)練輸入序列YTrain=labelSeq(1:numTrain);%訓(xùn)練標(biāo)簽XTest=sequenceData(:,:,numTrain+1:end);%測(cè)試輸入序列YTest=labelSeq(numTrain+1:end);%測(cè)試標(biāo)簽maxIter=50;%最大迭代popSize=20;%種群規(guī)模(鯨魚數(shù)量)inputSize=numFeatures;%輸入特征維度numHiddenUnits=64;%GRU隱藏單元數(shù)numFilters=32;%CNN卷積%訓(xùn)練參數(shù)maxEpochs=30;%最大訓(xùn)練輪數(shù)miniBatchSize=16;%批量大小%1.定義卷積門控循環(huán)單元(CNN-GRU)網(wǎng)絡(luò)結(jié)構(gòu)函數(shù)functionlayers=createCNN_GRU(numFilters,filterSize,numHiddensequenceInputLayer(inputSize,'Name','input')%特征維度convolution1dLayer(filterSize,numFilters,'P'Name’,'convl')%1D卷積層，提取局部時(shí)序特征，卷積核大小filterSize,數(shù)量numFilters,same填充保持尺寸batchNormalizationLayer('Name','batchnorm1')%定訓(xùn)練，加快收斂reluLayer('Name','relul')%ReLU激活層，引入非線性，防止梯度gruLayer(numHiddenUnits,'OutputMode’,'last','NaGRU層，記憶時(shí)間序列信息，隱藏單元數(shù)為numHiddenUnits,輸出最后時(shí)間步fullyConnectedLayer(numClasses,'Name’,'fc')%全softmaxLayer('Name','softmax')%SoftmaxclassificationLayer('Name','classoutput')%分類輸出%2.定義WOA優(yōu)化函數(shù)，用于尋找最佳超function[bestParams,bestAcc]=WOA_optimize(XTrain,YTrain,XinputSize,numClasses,maxI%超參數(shù)搜索空間邊界：numFilters[8,64],filterSize[ub=[64,5,128];%上界%初始化鯨魚群位置(解),每行一個(gè)個(gè)體，三維參數(shù)Positions(:,i)=1b(i)+(ub(%初始化適應(yīng)度(準(zhǔn)確率，越大越好)發(fā)numFilters=round(Positions(i,1)numHiddenUnits=round(Positions(i,3));layers=createCNN_GRU(numFilters,filterSiznumHiddenUnits,inputSize,numCloptions=trainingOptions('adam',...'MaxEpochs',10,...%為加快訓(xùn)練僅用10輪'MiniBatchSize',16,...'Shuffle','every-epoch',...'ValidationData',{XVal,YVal},...'ValidationFrequency',30,...'Verbose',false,...net=trainNetwork(XTrain,YTrain,layers,options);%YPred=classify(net,XVal,'MiniBatchPositions(i,:)=max(Positions(Positions(i,:)=min(Positions(%3.劃分訓(xùn)練集為訓(xùn)練和驗(yàn)證子集(WOA需要驗(yàn)證集評(píng)估)numTrainSamples=sizenumVal=floor(valRatio*numTrtrainIdx=idx(numVal+[bestParams,bestAcc]=WOA_optimize(YValSub,inputSize,numel(unique(YTrain)),maxIter,%5.用最優(yōu)參數(shù)訓(xùn)練最終模型bestNumFilters=round(bestPabestFilterSize=round(bestParams(2));bestNumHiddenUnits=round(bestParams(3));finalLayers=createCNN_GRU(bestNbestNumHiddenUnits,inputSize,numel(unique(YTrafinalOptions=trainingOptions('adam',...'MiniBatchSize’,miniBatchSize,...'Shuffle’,'every-epoch',...'Verbose',true,...finalNet=trainNetwork(XTrain,YTrain,finalLayers,finalOptions);%評(píng)估模型在測(cè)試集上的性能(用訓(xùn)練好的模型進(jìn)行預(yù)測(cè))YPredTest=classify(finalNet,XTest,'MiniBatchSize',mini使用訓(xùn)練好的模型對(duì)測(cè)試集進(jìn)行分類預(yù)測(cè)YTestNumeric=grp2idx(YTest);%將測(cè)試標(biāo)簽轉(zhuǎn)換為數(shù)字索引形式，便于計(jì)算指標(biāo)YPredNumeric=grp2idx(YPredTest);%將預(yù)測(cè)標(biāo)簽轉(zhuǎn)復(fù)制%將類別轉(zhuǎn)為數(shù)值后計(jì)算誤差errors=YPredNumeric-YTestNumeric;%預(yù)測(cè)誤差向量%MSE:均方誤差MSE=mean(errors.^2);%平均誤差平方，衡量預(yù)測(cè)偏差大小MAE=mean(abs(errors));%平均絕對(duì)誤差，衡量預(yù)測(cè)偏差的絕對(duì)值大小%MAPE:平均絕對(duì)百分比誤差(分母非零樣本)nonZeroIdx=YTestNumeric~=0;%非零標(biāo)簽索引MAPE=mean(abs(errors(nonZeroIdx)./YTestNumeric(nonZeroId百分比誤差MBE=mean(errors);%誤差均值，顯示系統(tǒng)性偏差方向%R2:決定系數(shù)，擬合優(yōu)度指標(biāo)SStot=sum((YTestNumeric-mean(YTestNumeric)).^2)SSres=sum(errors.^2);%殘差平方和R2=1-SSres/SStot;%R2值，越接近1越好%VaR(風(fēng)險(xiǎn)價(jià)值)計(jì)算(這里用誤差絕對(duì)值的95%分位數(shù)表示)VaR=quantile(abs(errors),0.95);%誤差絕對(duì)值95%分位數(shù)，反映極端誤%ES(期望短缺),VaR以上誤差的平均值ES=mean(abs(ererrorMatrix=abs(errors);%絕對(duì)誤差構(gòu)成矩陣imagesc(errorMatrix');%顯示誤差矩陣的熱圖(轉(zhuǎn)置更直觀)colorbar;%添加顏色條，標(biāo)示誤差大小title('預(yù)測(cè)誤差熱圖’);%圖標(biāo)題xlabel('樣本編號(hào)’);%x軸標(biāo)簽ylabel('誤差值’);%y軸標(biāo)簽scatter(1:length(errors),errors,'filled');%殘差plot([1length(errors)],[00],'r--','LineWidttitle('殘差圖’);%標(biāo)題ylabel('殘差值(預(yù)測(cè)-真實(shí))’);%y軸metrics=[MSE,MAE,MAPE,MBE,R2,VaR,ES];%性能指標(biāo)數(shù)組metricNames={'MSE','MAE','MAPE(%)’,'MBE','R^2','VaR','ES'set(gca,'XTickLabel',metricNames,'XTick',1:numel(metricNames));%置x軸刻度標(biāo)簽第五階段：精美GUI界面精美GUI界面'NumberTitle','off',...'MenuBar','none’,...'ToolBar','none’,...'Resize’,'on',...handles.trainInfo=[];%數(shù)據(jù)文件選擇按鈕handles.btnLoadData=uicontrol('Style','pushbutton',...'String','選擇數(shù)據(jù)文件’,...'FontSize',11,...'Position',[3054012030],...%數(shù)據(jù)文件路徑顯示文本框(只讀)handles.txtDataPath=uicontrol('Style','edit',...'Position',[16054070030],...'Enable’,'inactive',...'BackgroundColor','white',...%模型參數(shù)設(shè)置區(qū)標(biāo)簽uicontrol('Style','text',...'String','模型參數(shù)設(shè)置’,...'FontSize',13,'FontWeight','bold',...%學(xué)習(xí)率標(biāo)簽及輸入框uicontrol('Style','text',...'String','學(xué)習(xí)率(learningrate):',...'FontSize',11,...handles.editLearningRate=uicontrol('Style','edit',...'String','0.001',...25],...%批量大小標(biāo)簽及輸入框uicontrol('Style’,'text',...'FontSize',11,...handles.editBatchSize=uicontrol('Style’,'edit',...'String','16',...%迭代次數(shù)標(biāo)簽及輸入框uicontrol('Style’,'text',...'FontSize’,11,...handles.editMaxEpochs=uicontrol('Style’,'edit',...'String',’30',...uicontrol('Style’,'text',...'String','WOA迭代次數(shù)：',...'FontSize',11,...handles.editWOAIter=uicontrol('Style’,'edit',...'String',’50',...'Position',[1404108025],...uicontrol('Style’,'text',...'String','WOA種群規(guī)模：',...'FontSize',11,...handles.editWOAPop=uicontrol('Style','edit',...'String','20',...'Position',[3604108025],...%模型訓(xùn)練按鈕handles.btnTrain=uicontrol('Style’,'pushbutton',...'String','開始訓(xùn)練’,...'FontSize',12,...'Position',[48041012030],...%訓(xùn)練狀態(tài)顯示文本框(多行，禁編輯)handles.txtTrainStatus=uicontrol('Style','edit',...'Max',2,...'Enable’,'inactive’,...'BackgroundColor','white',...'FontSize’,10,...%預(yù)測(cè)結(jié)果顯示區(qū)域標(biāo)簽uicontrol('Style','text''String',’預(yù)測(cè)結(jié)果與評(píng)估指標(biāo)’,...'FontSize’,13,'FontWeight','bold',...%預(yù)測(cè)準(zhǔn)確率顯示文本框uicontrol('Style’,'text''String',’測(cè)試準(zhǔn)確率：',...'FontSize',11,...handles.txtAccuracy=uicontrol('Style’,'edit',...'String','',...'Position',[12020010025],...'Enable’,'inactive’,...'BackgroundColor','white',...%其他評(píng)估指標(biāo)顯示(MSE,MAE,R2等)metricLabels={'MSE:','MAE:','MAPE(%):','MBE:','R^2:handles.txtMetrics=gobjects(length(metricLuicontrol('Style’,'text',...'String',metricLabels{i},...'FontSize',11,...'Position',[30handles.txtMetrics(i)=uicontrol('Style','edit',...'String',’’,...25],...'Enable’,'inactive’,...'white',...%結(jié)果導(dǎo)出按鈕=uicontrol('Style’,'pushbut'String',’導(dǎo)出結(jié)果’,...'FontSize’,12,...'Position',[305010030],...'Callback',@exportRe=uicontrol('Style','text',...'String','等待用戶操作...',...'FontSize',10,...'Position',[1505071030],...%動(dòng)態(tài)布局調(diào)整回調(diào)函數(shù)set(hFig,'SizeChangedFcn',@(src,event)resize%參數(shù)合法性校驗(yàn)lr=str2double(get(handles.editLearningRate,'String'));batchSize=str2double(get(handles.editBatchSize,'String'));maxEpochs=str2double(get(handles.editMaxEpochs,'String’));WOAIter=str2double(get(handles.editWOAIter,'String'));WOAPop=str2double(get(handles.editWOAPop,'String'));ifany(isnan([1r,batchSize,maxEpochs,WOAIter,WOAPop]))lr<=0||batchSize<=0||maxEpochs<=0||WOerrordlg('請(qǐng)輸入有效的正數(shù)參數(shù)!’,’參數(shù)錯(cuò)誤’);%參數(shù)無效%顯示訓(xùn)練狀態(tài)set(handles.txtTrainStatus,'String',’開始數(shù)據(jù)預(yù)處理和模型訓(xùn)練，請(qǐng)稍候...');%訓(xùn)練狀態(tài)提示%數(shù)據(jù)預(yù)處理一此處以前面代碼為例，轉(zhuǎn)換為訓(xùn)練和測(cè)試集%數(shù)據(jù)預(yù)處理：按前述邏輯進(jìn)行窗口化、歸一化、標(biāo)簽提取%取數(shù)值部分，排除標(biāo)簽列為第一列dataArray=table2array(dataTable(:,2:end));labels=table2arnumSamples=size(danumFeatures=size(danumWindows=floor((numSamples-windowSize)/stsequenceData=zeros(windowSize,numFeatures,numWindows);sequenceData(:,:,i)=dataArray(i%歸一化minVals=min(dataArray);sequenceData(:,i,:)=(sequenceData(/(maxVals(i)-minVals(i)+%劃分訓(xùn)練測(cè)試集80%訓(xùn)練20%測(cè)試numTrain=floor(trainRatio*numWindows);XTrain=sequenceData(:,:,1:numTraYTrain=labelSeq(1:numTXTest=sequenceData(:,:,numTrain+1:eYTest=labelSeq(numTrain+1%將標(biāo)簽轉(zhuǎn)換為分類類別handles.XTrain=XTrain;handles.YTrain=YTrset(handles.txtTrainStatus,'String',’數(shù)據(jù)預(yù)處理完成，開始WOA優(yōu)化CNN-GRU模型參數(shù)...');errordlg([’數(shù)據(jù)預(yù)處理失?。?,ME.message],'錯(cuò)誤’);%調(diào)用WOA優(yōu)化函數(shù)，注意這里簡(jiǎn)化為局部函數(shù)numClasses=numel(categorie%劃分訓(xùn)練子集和驗(yàn)證集(20%驗(yàn)證)numTrainSamples=size(numVal=floor(valRatio*numTrainSidxShuffle=randperm(numTrainSamples);valIdx=idxShuffle(1:numVal);trainIdx=idxShuffle(numVal+1:end);XTrainSub=XTrain(:,:,trainIdx);YTrainSub=YTrain(traiXValSub=XTrain(:,:,valYValSub=YTrain(valIdx);%使用之前定義的WOA優(yōu)化函數(shù)(放在GUI文件末尾，確?？烧{(diào)用)[bestParams,bestAcc]=WOA_optiXValSub,YValSub,inputSize,numClasses,maxIter,p%顯示優(yōu)化完成信息set(handles.txtTrainStatus,'String',sprintf('WOA優(yōu)化完成，最佳驗(yàn)證準(zhǔn)確率：%.4f,開始最終訓(xùn)練...',bestAcc));%使用最佳參數(shù)訓(xùn)練最終模型bestNumFilters=round(bestParams(1));bestFilterSize=round(bestPabestNumHiddenUnits=round(bestParams(3));layers=createCNN_GRU(bestNumFilters,bestFilterSize,bestNumHiddenUnits,inputSize,numClasoptions=trainingOptions('adam3,...'MaxEpochs',maxEpochs,...'MiniBatchSize',batchSize,...'Shuffle’,'every-epoch',...'Verbose',false,...net=trainNetwork(XTrain,YTrain,layers,options);%預(yù)測(cè)測(cè)試集YPred=classify(net,XTest,'MiniBatchSize’,batchSi%計(jì)算準(zhǔn)確率set(handles.txtAccuracy,'String',num2%計(jì)算并顯示其他指標(biāo)YPredNum=grp2idx(errors=YPredNum-MAE=mean(abs(erMAPE=mean(abs(errors(nonZeroIdx)./YTestNum(nonZSStot=sum((YTestNum-mean(YTestNum)).^2);R2=1-SSres/SStot;VaR=quantile(abs(errors),0.95);ES=mean(abs(errors(abs(errors)>VaR)));metrics=[MSE,MAE,MAPE,MBE,R2,VaR,ES];fori=1:length(set(handles.txtMetrnum2str(metrics(i),’%.4set(handles.txtTrainStatus,'String','訓(xùn)練完成，結(jié)果已更errordlg([’訓(xùn)練失?。?,ME.message],'錯(cuò)誤’);functionexportResultsCallback(~,~)ifisempty()msgbox('請(qǐng)先訓(xùn)練模型后再導(dǎo)出結(jié)果?！?’提示’,'warn’);[file,path]=uiputfile('*.mat',’保存訓(xùn)練模型及結(jié)果’);ifisequal(file,0)return;%用戶取消保存save(fullfile(path,file),'handles');%保存所有handles數(shù)據(jù)，包括模型和結(jié)果msgbox('模型及結(jié)果保存成功?！?’提示’,'help');%動(dòng)態(tài)調(diào)整控件位置大小figPos=get(hFig,'Positiowidth=figPos(3);height=figPos(4%數(shù)據(jù)路徑文本寬度調(diào)整set(handles.txtDataPath,'Position',[160height-60%訓(xùn)練狀態(tài)框?qū)挾群透叨日{(diào)整set(handles.txtTrainStatus,'Position',[30height-320width-60%預(yù)測(cè)指標(biāo)框布局fori=1:length(handles.set(handles.txtMetrics(i),'Position',[120yPos%輔助函數(shù)：WOA優(yōu)化函數(shù)和CNN-GRU創(chuàng)建函數(shù)functionlayers=createCNN_GRU(numFilters,filterSizsequenceInputLayer(inputSiconvolution1dLayer(filterSize,numFilters,'PbatchNormalizationLayer('Name','batchnreluLayer('Name’,'regruLayer(numHiddenUnits,'OutputMode’,'last','fullyConnectedLayer(numClasclassificationLayer('Name'function[bestParams,bestAcc]=WOA_optimize(XTrain,YTrain,XVal,YVal,inputSize,numClasses,maxIPositions(:,i)=lb(i)+(ub(i)-1b(i))*rand(bestParams=zeros(1,3);numFilters=round(PositifilterSize=round(Positions(i,2));numHiddenUnits=round(Positions(i,3));layers=createCNN_GRU(numFilters,filterSiznumHiddenUnits,inputSize,numCloptions=trainingOptions('adam',...'MaxEpochs',10,...'MiniBatchSize’,16,...'Shuffle','every-epoch',...'ValidationData',{XVal,YVal},...'Verbose',false,...net=trainNetwork(XTrain,YTrain,layers,optYPred=classify(net,XVal,'MiniBatchSize',16);ifacc>bestAccifp<0.5ifabs(A)<1Positions(i,:)=bestParamsD=abs(C*X_rand-Positions(i,:));Positions(i,:)=D*exp(b*1)*cos(2*pi*1)+bePositions(i,:)=max(PositionPositions(i,:)=min(Positions(第六階段：防止過擬合及參數(shù)調(diào)整防止過擬合functionlayers=createCNN_GRU(numFilters,filterSizconvolution1dLayer(filterSize,numFilters,'P'Name’,'conv1',...用He初始化權(quán)重batchNormalizationLayer('Name','batchnodropoutLayer(0.3,'Name','dropout1')%Dropout層，丟棄30%的gruLayer(numHiddenUnits,'OutputMode','last’,'NGRU層，捕捉時(shí)間序列長(zhǎng)依賴dropoutLayer(0.3,'Name','dropout2')%GRU后再加一層Dropout,fullyConnectedLayer(numClasses,'Name','fc',...'WeightLearnRateFactor',1,'BiasLearnRateFactor',1,...'WeightL2Factor',0.01)%全連接層，帶有L2正則化因子0.01softmaxLayer('Name','softmax')%SoftmaxclassificationLayer('Name','classoutput')%交叉熵?fù)p失層%在訓(xùn)練選項(xiàng)中添加EarlyStopping監(jiān)控驗(yàn)證集性能，實(shí)現(xiàn)早停options=trainingOptions('adam,...'MaxEpochs',maxEpochs,...'MiniBatchSize',batchSize,...'Shuffle','every-epoch',...'ValidationData',{XValSub,YValSub},...'ValidationFrequency',30,...'Verbose',true,...'ExecutionEnvironment','auto',...'Plots','training-progress',...'OutputFcn',@(info)stopIfNoImprovement(info,5));%若驗(yàn)證集連續(xù)5functionstoppersistentbestValAccifinfo.State=="start"elseifinfo.State=="iteration"if~isempty(info.ValidationAccuracy)ifinfo.ValidationAccuracy>bestValAccbestValAcc=info.ValidationAccuracy;ifwait>=N超參數(shù)調(diào)整復(fù)制%采用K折交叉驗(yàn)證調(diào)整超參數(shù)functionbestHyperparams=crossValidateHyperparams(data,labelindices=crossvalind('Kfold',labels,kFold);%生成k折索引bestHyperparams=struct('numFilters',32,'filterSize’,3,fornumHiddenUnits=[32,64,128]XTrainCV=data(:,:,trainIdx);YTrainCV=labels(traiXTestCV=data(:,:,teslayers=createCNN_GRU(numFnumHiddenUnits,size(data,2),numel(unique(laboptions=trainingOptions('adam',...'MaxEpochs',10,...'MiniBatchSize',16,...'Verbose',false,...net=trainNetwork(XTrainCV,YTraiYPred=classify(net,XTestCV);acc=sum(YPred==YTestCV)/numel(YTestCV);meanAcc=accSum/kFold;ifmeanAcc>bestAccbestHyperparams.numFilters=numFilters;bestHyperparams.filterSize=fbestHyperparams.numHiddenUnits=numHiddenUnits;增加數(shù)據(jù)集function[XTrainFull,YTrainFull]=combineDXTrainFull=cat(3,XTrainFull,datasets{i}.XTrain);YTrainFull=[YTrainFul1;datasets{i}.優(yōu)化超參數(shù)%如：輸入延遲inputDelay[1,5],反饋延遲feedbackDelay[1,5]%示例參數(shù)邊界擴(kuò)展functionWOA_CNN_GRU_Clas%%環(huán)境準(zhǔn)備clearvars;%清除所有變量，確保環(huán)境干凈無殘留影響warning('off','all');%關(guān)閉所有警告信息，避免干擾輸出closeall;%關(guān)閉所有圖形窗口，保持界面整潔%檢查必要工具箱requiredToolboxes={'DeepLearningToolbox','StatisticsandMachineLearningToolbox','ParallelComputinV=ver;%獲取已安裝工具箱信息installedToolboxes={v.Name};%提取名稱列表if~any(contains(installedToolboxes,requiredToolboxes{k}))error(['缺少工具箱：',requiredToolboxes{k},’請(qǐng)先安裝后再運(yùn)行’]);%無必要工具箱報(bào)錯(cuò)停止執(zhí)行ifgpuDeviceCount>0gpuDevice(1);%激活第一個(gè)GPU,提升深度學(xué)習(xí)計(jì)算速度disp('未檢測(cè)到GPU設(shè)備，使用CPU進(jìn)行計(jì)算’);%無GPU時(shí)提示使用%%數(shù)據(jù)準(zhǔn)備%導(dǎo)入數(shù)據(jù)文件，自動(dòng)根據(jù)擴(kuò)展名選擇讀取方式dataFile='dataset.csv';%請(qǐng)?zhí)鎿Q為ifendsWith(dataFile,'.csv')loadedData=load(dataFile);%加載MAT文件vars=fieldnames(loadedData);%處理數(shù)據(jù)，轉(zhuǎn)換為數(shù)值矩陣和標(biāo)簽數(shù)組dataArray=table2array(dataTable(:,2:end));%特征數(shù)據(jù)，從第2列開始labels=table2array(dataTable(:,1));%標(biāo)簽列為第1列%檢測(cè)并填補(bǔ)缺失值，使用線性插值X=dataAnanIdx=isnan(x);%找缺失值位置ifany(nanIdx)x(nanIdx)=interp1(find(~nanIdx),x(~nanIdx),find(ndataArray(:,feat)=x;%更新特征列%異常值檢測(cè)與處理，3倍標(biāo)準(zhǔn)差法，替換為中位數(shù)ifany(outlierIdx)x(outlierIdx)=med;%替換異常值%時(shí)間序列窗口化參數(shù)windowSize=30;%窗口長(zhǎng)度為30stepSize=1;%窗口滑動(dòng)步長(zhǎng)為1numSamples=size(danumFeatures=size(danumWindows=floor((numSamples-windowSize)/stepSize)+1;%構(gòu)造三維數(shù)據(jù)窗口[時(shí)間步長(zhǎng)，特征數(shù)，窗口個(gè)數(shù)]sequenceData=zeros(windowSize,numFeatures,numWindows);idxEnd=idxStart+windowSize-1;sequenceData(:,:,i)=dataArray(i%對(duì)每個(gè)特征做歸一化minVals=min(dataArray);sequenceData(:,feat,:)=(sequminVals(feat))./(maxVals(feat)-minVals(fe%標(biāo)簽對(duì)應(yīng)窗口結(jié)束時(shí)間點(diǎn)labelSeq=labels(windowSize:end);%轉(zhuǎn)為分類變量labelSeqCat=categorical(la%劃分訓(xùn)練集和測(cè)試集(80%訓(xùn)練，20%測(cè)試)numTrain=floor(trainRatio*numWindows);XTrain=sequenceData(:,:,YTrain=labelSeqCat(1:numTrain);XTest=sequenceData(:,:,numTrain+1:end);YTest=labelSeqCat(numTlb=[8,2,16];%numFilters,filterSize[bestParams,bestAcc]=WOA_optpopSize,lb,ub,numFeatures,numel(categories(YTrabestNumFilters=round(bestPabestNumHiddenUnits=round(bestParams(3));layers=createCNN_GRU(bestNumFilters,bestFilterSize,bestNumHiddenUnits,numFeatures,numel(categories(YTr%訓(xùn)練選項(xiàng)，含早停與L2正則化options=trainingOptions('adam',...'MaxEpochs',30,...'MiniBatchSize',16,...'Shuffle','every-epoch',...'ValidationData',{XTest,YTest},...'ValidationFrequency',30,...'Verbose',true,...'Plots','training-progress',...'ExecutionEnvironment','auto',...'L2Regularization',0.01,...'OutputFcn',@(info)stopIfNoImprovement(info,5));%驗(yàn)證集5%訓(xùn)練最終模型net=trainNetwork(XTrain,YTrain,layers,options);%%模型預(yù)測(cè)及性能評(píng)估YPred=classify(net,XTest,'MiniBatchSize',16);%轉(zhuǎn)換為數(shù)值標(biāo)簽用于誤差計(jì)算%計(jì)算性能指標(biāo)nonZeroIdx=YTestNum~=0;MAPE=mean(abs(errors(nonZeroIdx)./YTestNum(nonZeroIdx)))*100;VaR=quantile(abs(errors),0.95);ES=mean(abs(errors(abs%顯示指標(biāo)fprintf('MSE:%.4f,MAE:%.4f,MAPE:%.4f%%,MBE:VaR:%.4f,ES:%.4f\n',...MSE,MAE,MAPE,MBE,R2,Va%%精美GUI界面集成(簡(jiǎn)易版)%簡(jiǎn)單啟動(dòng)GUI方便演示W(wǎng)OA_CNN_GRU_GUI(net,XTest,%%內(nèi)部函數(shù)定義functionlayers=createCNN_GRU(numFilters,filterSize,numHiddenUnits,inputSize,numCl%構(gòu)建CNN-GRU網(wǎng)絡(luò)結(jié)構(gòu)，含Dropout和L2正則化防過擬合sequenceInputLayer(inputSize,'Name','in