SSA-CNN-BiLSTM麻雀算法优化卷积双向长短期记忆神经网络时间序列预测（Matlab完整程序和数据）

clc close all clear all data=xlsread('B05.xlsx',1,'A2:A169'); numTimeStepsTrain = floor(0.7*numel(data)); dataTrain = data(1:numTimeStepsTrain,:);%选择训练集 dataTest = data(numTimeStepsTrain+1:end,:); % 选择测试集 k =9; %滑动窗口设置为9 具体设多少需要衡量 m = 1 ; %预测步长 for i = 1:size(dataTrain)-k-m+1 XTrain(:,i) = dataTrain(i:i+k-1,:); YTrain(:,i) = dataTrain(i+k:i+k+m-1,:); end for i = 1:size(dataTest)-k-m+1 XTest(:,i) = dataTest(i:i+k-1,:); YTest(:,i) = dataTest(i+k:i+k+m-1,:); end x = XTrain; y = YTrain; [xnorm,xopt] = mapminmax(x,0,1); [ynorm,yopt] = mapminmax(y,0,1); x = x'; % 转换成2-D image for i = 1:length(ynorm) Train_xNorm{i} = reshape(xnorm(:,i),k,1,1); Train_yNorm(:,i) = ynorm(:,i); Train_y(i,:) = y(:,i); end Train_yNorm= Train_yNorm'; xtest = XTest; ytest = YTest; [xtestnorm] = mapminmax('apply', xtest,xopt); [ytestnorm] = mapminmax('apply',ytest,yopt); xtest = xtest'; for i = 1:length(ytestnorm) Test_xNorm{i} = reshape(xtestnorm(:,i),k,1,1); Test_yNorm(:,i) = ytestnorm(:,i); Test_y(i,:) = ytest(:,i); end Test_yNorm = Test_yNorm'; %% SSA优化参数设置 SearchAgents = 10; % 种群数量 50 Max_iterations = 30 ; % 迭代次数 10 lowerbound = [1e-10 0.001 10 ];%三个参数的下限 upperbound = [1e-2 0.01 200 ];%三个参数的上限 dimension = 3;%数量，即要优化的LSTM参数个数 %% SSA优化LSTM % [Best_pos,Best_score,Convergence_curve]=SSA(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,ObjFcn) %% 参数设置 ST = 0.8; % 预警值 PD = 0.2; % 发现者的比列，剩下的是加入者 PDNumber = SearchAgents * PD; % 发现者数量 SDNumber = SearchAgents - SearchAgents * PD; % 意识到有危险麻雀数量 %% 判断优化参数个数 if(max(size(upperbound)) == 1) upperbound = upperbound .* ones(1, dimension); lowerbound = lowerbound .* ones(1, dimension); end %% 种群初始化 pop_lsat = initialization(SearchAgents, dimension, upperbound, lowerbound); pop_new = pop_lsat; %% 计算初始适应度值 fitness = zeros(1, SearchAgents); for i = 1 : SearchAgents fitness(i) = fun(pop_new(i,:),Train_xNorm,Train_yNorm,Test_xNorm,Test_y,yopt,k); end %% 得到全局最优适应度值 [fitness, index]= sort(fitness); GBestF = fitness(1); %% 得到全局最优种群 for i = 1 : SearchAgents pop_new(i, :) = pop_lsat(index(i), :); end GBestX = pop_new(1, :); X_new = pop_new; %% 优化算法 for i = 1: Max_iterations BestF = fitness(1); R2 = rand(1); for j = 1 : PDNumber if(R2 < ST) X_new(j, :) = pop_new(j, :) .* exp(-j / (rand(1) * Max_iterations)); else X_new(j, :) = pop_new(j, :) + randn() * ones(1, dimension); end end for j = PDNumber + 1 : SearchAgents if(j > (SearchAgents - PDNumber) / 2 + PDNumber) X_new(j, :) = randn() .* exp((pop_new(end, :) - pop_new(j, :)) / j^2); else A = ones(1, dimension); for a = 1 : dimension if(rand() > 0.5) A(a) = -1; end end AA = A' / (A * A'); X_new(j, :) = pop_new(1, :) + abs(pop_new(j, :) - pop_new(1, :)) .* AA'; end end Temp = randperm(SearchAgents); SDchooseIndex = Temp(1 : SDNumber); for j = 1 : SDNumber if(fitness(SDchooseIndex(j)) > BestF) X_new(SDchooseIndex(j), :) = pop_new(1, :) + randn() .* abs(pop_new(SDchooseIndex(j), :) - pop_new(1, :)); elseif(fitness(SDchooseIndex(j)) == BestF) K = 2 * rand() -1; X_new(SDchooseIndex(j), :) = pop_new(SDchooseIndex(j), :) + K .* (abs(pop_new(SDchooseIndex(j), :) - ... pop_new(end, :)) ./ (fitness(SDchooseIndex(j)) - fitness(end) + 10^-8)); end end %% 边界控制 for j = 1 : SearchAgents for a = 1 : dimension if(X_new(j, a) > upperbound(a)) X_new(j, a) = upperbound(a); end if(X_new(j, a) < lowerbound(a)) X_new(j, a) = lowerbound(a); end end end %% 获取适应度值 for j = 1 : SearchAgents fitness_new(j) = fun(X_new(j, :),Train_xNorm,Train_yNorm,Test_xNorm,Test_y,yopt,k); end %% 获取最优种群 for j = 1 : SearchAgents if(fitness_new(j) < GBestF) GBestF = fitness_new(j); GBestX = X_new(j, :); end end %% 更新种群和适应度值 pop_new = X_new; fitness = fitness_new; %% 更新种群 [fitness, index] = sort(fitness); for j = 1 : SearchAgents pop_new(j, :) = pop_new(index(j), :); end %% 得到优化曲线 curve(i) = GBestF; avcurve(i) = sum(curve) / length(curve); end %% 得到最优值 Best_pos = GBestX; Best_score = curve(end); %% 得到最优参数 NumOfUnits =abs(round( Best_pos(1,3))); % 最佳神经元个数 InitialLearnRate = Best_pos(1,2) ;% 最佳初始学习率 L2Regularization = Best_pos(1,1); % 最佳L2正则化系数 % inputSize = k; outputSize = 1; %数据输出y的维度 layers = [ ... sequenceInputLayer([inputSize,1,1],'name','input') %输入层设置 sequenceFoldingLayer('name','fold') convolution2dLayer([2,1],10,'Stride',[1,1],'name','conv1') batchNormalizationLayer('name','batchnorm1') reluLayer('name','relu1') convolution2dLayer([1,1],10,'Stride',[1,1],'name','conv2') batchNormalizationLayer('name','batchnorm2') reluLayer('name','relu2') maxPooling2dLayer([1,3],'Stride',1,'Padding','same','name','maxpool') sequenceUnfoldingLayer('name','unfold') flattenLayer('name','flatten') bilstmLayer(NumOfUnits ,'Outputmode','sequence','name','hidden1') dropoutLayer(0.3,'name','dropout_1') bilstmLayer(NumOfUnits ,'Outputmode','sequence','name','hidden3') dropoutLayer(0.3,'name','dropout_3') bilstmLayer(NumOfUnits ,'Outputmode','last','name','hidden2') dropoutLayer(0.3,'name','drdiopout_2') fullyConnectedLayer(outputSize,'name','fullconnect') % 全连接层设置（影响输出维度）（cell层出来的输出层） % tanhLayer('name','softmax') regressionLayer('name','output')]; lgraph = layerGraph(layers) lgraph = connectLayers(lgraph,'fold/miniBatchSize','unfold/miniBatchSize'); % 参数设置 % options = trainingOptions('adam', ... % 优化算法Adam % 'MaxEpochs', 2000, ... % 最大训练次数 % 'GradientThreshold', 1, ... % 梯度阈值 % 'InitialLearnRate', InitialLearnRate, ... % 初始学习率 % 'LearnRateSchedule', 'piecewise', ... % 学习率调整 % 'LearnRateDropPeriod', 850, ... % 训练850次后开始调整学习率 % 'LearnRateDropFactor',0.2, ... % 学习率调整因子 % 'L2Regularization', L2Regularization, ... % 正则化参数 % 'ExecutionEnvironment', 'cpu',... % 训练环境 % 'Verbose', 0, ... % 关闭优化过程 % 'Plots', 'training-progress'); % 画出曲线 opts = trainingOptions('adam', ... 'MaxEpochs',500, ... 'GradientThreshold',1,... 'ExecutionEnvironment','cpu',... 'InitialLearnRate',InitialLearnRate, ... 'LearnRateSchedule','piecewise', ... 'LearnRateDropPeriod',100, ... %2个epoch后学习率更新 'LearnRateDropFactor',0.5, ... 'Shuffle','once',... % 时间序列长度 'SequenceLength',1,... 'MiniBatchSize',128,... 'Verbose',1,... 'Plots','training-progress'); % 网