机器学习之MATLAB代码--基于MISO的SSA-CNN-LSTM(八)

机器学习之MATLAB代码--基于MISO的SSA-CNN-LSTM(八)

  • 代码
    • 数据
      • 结果

代码

总的代码顺序,顺序而下:机器学习之MATLAB代码--基于MISO的SSA-CNN-LSTM(八)_第1张图片

1、


clc
close all
clear all


%训练数据
data =  xlsread('load data3.xlsx',1,'A2:G2001');
input = data(:,1:6);
output = data(:,7);

numTimeStepsTrain = floor(0.7*numel(data(:,1)));

XTrain = input(1:numTimeStepsTrain,:);
YTrain = output(1:numTimeStepsTrain,:);

XTest = input(numTimeStepsTrain+1:end,:);
YTest = output(numTimeStepsTrain+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),6,1,1);
    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),6,1,1);
  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);
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);
   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 = 6;
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')
    lstmLayer(NumOfUnits ,'Outputmode','sequence','name','hidden1') 
    dropoutLayer(0.3,'name','dropout_1')
    lstmLayer(NumOfUnits ,'Outputmode','sequence','name','hidden3') 
    dropoutLayer(0.3,'name','dropout_3')
    lstmLayer(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');

% 网络训练
tic

net = trainNetwork(Train_xNorm,Train_yNorm,lgraph,opts );




Predict_Ynorm_Train = net.predict(Train_xNorm);
Predict_Y_Train  = mapminmax('reverse',Predict_Ynorm_Train',yopt);
Predict_Y_Train = Predict_Y_Train';

figure
hold on 
plot(Predict_Y_Train,'r-','LineWidth',2.0)
plot(Train_y,'b-','LineWidth',2.0);    
ylabel('kW')
legend('预测值','实际值')
title('训练')





Predict_Ynorm = net.predict(Test_xNorm);
Predict_Y  = mapminmax('reverse',Predict_Ynorm',yopt);
Predict_Y = Predict_Y';


figure
hold on 
plot(Predict_Y,'r-','LineWidth',2.0)
plot(Test_y,'b-','LineWidth',2.0)
legend('预测值','实际值')
ylabel('kW')
title('测试')


% 预测结果评价
ae= abs(Predict_Y - Test_y);
rmse = (mean(ae.^2)).^0.5;
mse = mean(ae.^2);
mae = mean(ae);
mape = mean(ae./Predict_Y);

disp('机组预测结果评价指标:')
disp(['RMSE = ', num2str(rmse)])
disp(['MSE  = ', num2str(mse)])
disp(['MAE  = ', num2str(mae)])
disp(['MAPE = ', num2str(mape)])


disp(['最佳神经元个数为:', num2str(NumOfUnits)])
disp(['最佳初始学习率为:', num2str(InitialLearnRate)])
disp(['最佳L2正则化系数为:', num2str(L2Regularization)])

2、

function Positions = initialization(SearchAgents_no, dim, ub, lb)

%%  初始化

%%  待优化参数个数
Boundary_no = size(ub, 2); 

%%  若待优化参数个数为1
if Boundary_no == 1
    Positions = rand(SearchAgents_no, dim) .* (ub - lb) + lb;
end

%%  如果存在多个输入边界个数
if Boundary_no > 1
    for i = 1 : dim
        ub_i = ub(i);
        lb_i = lb(i);
        Positions(:, i) = rand(SearchAgents_no, 1) .* (ub_i - lb_i) + lb_i;
    end
end

3、

function y = fun(x,Train_xNorm,Train_yNorm,Test_xNorm,Test_y,yopt)
%函数用于计算粒子适应度值


%rng default;%固定随机数

numhidden_units1 =  fix(x(3))+1; % 隐含层神经元数量 round为四舍五入函数;
numhidden_units2= fix(x(3))+1;



%  层设置,参数设置
inputSize = 6;
outputSize = 1;  %数据输出y的维度  




options = trainingOptions('adam', ...
    'MaxEpochs',100, ...
    'GradientThreshold',1,...
    'ExecutionEnvironment','cpu',...
    'InitialLearnRate',x(2), ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropPeriod',100, ...   %100个epoch后学习率更新
    'LearnRateDropFactor',0.5, ...
    'Shuffle','once',...  % 时间序列长度
    'SequenceLength',1,...
    'MiniBatchSize',128,...
    'L2Regularization', x(1), ... '
    'Verbose',1);
%% lstm

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')
    lstmLayer(numhidden_units1,'Outputmode','sequence','name','hidden1') 
    dropoutLayer(0.3,'name','dropout_1')
    lstmLayer(numhidden_units1,'Outputmode','sequence','name','hidden3') 
    dropoutLayer(0.3,'name','dropout_3')
    lstmLayer(numhidden_units2,'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');

%
% 网络训练


net = trainNetwork(Train_xNorm,Train_yNorm,lgraph,options);

Predict_Ynorm = net.predict(Test_xNorm);
Predict_Y  = mapminmax('reverse',Predict_Ynorm',yopt);
Predict_Y = Predict_Y';

% rmse_without_update1 = sqrt(mean((Predict_Y(1,:)-Test_y(1,:))).^2,'ALL')
rmse_without_update1 = sqrt(mean((Predict_Y(1,:)-(Test_y(1,:))).^2,'ALL'))
y = rmse_without_update1 ;%  cost为目标函数 ,目标函数为rmse
end

4、


clc
close all
clear all


data =  xlsread('load data3.xlsx',1,'A2:G2001');
input = data(:,1:6);
output = data(:,7);

inputSize = 6;
outputSize = 1;  

numTimeStepsTrain = floor(0.7*numel(data(:,1)));

XTrain = input(1:numTimeStepsTrain,:);
YTrain = output(1:numTimeStepsTrain,:);

XTest = input(numTimeStepsTrain+1:end,:);
YTest = output(numTimeStepsTrain+1:end,:);




x = XTrain;
y = YTrain;

[xnorm,xopt] = mapminmax(x',0,1);
[ynorm,yopt] = mapminmax(y',0,1);
x = x';


for i = 1:length(ynorm)

    Train_xNorm{i} = reshape(xnorm(:,i),inputSize,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),inputSize,1,1);
  Test_yNorm(:,i) = ytestnorm(:,i);
  Test_y(i,:) = ytest(i,:);
end
Test_yNorm = Test_yNorm';



NumOfUnits =100;       
InitialLearnRate = 0.005; 
% 


layers = [ ...
    
    sequenceInputLayer([inputSize,1,1],'name','input')   %
    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')
    
    lstmLayer(NumOfUnits ,'Outputmode','sequence','name','hidden1') 
    dropoutLayer(0.3,'name','dropout_1')
    
    lstmLayer(NumOfUnits ,'Outputmode','sequence','name','hidden3') 
    dropoutLayer(0.3,'name','dropout_3')
    
    lstmLayer(NumOfUnits ,'Outputmode','last','name','hidden2') 
    dropoutLayer(0.3,'name','drdiopout_2')
    
    fullyConnectedLayer(outputSize,'name','fullconnect')   % 鍏ㄨ繛鎺ュ眰璁剧疆锛堝奖鍝嶈緭鍑虹淮搴︼級锛坈ell灞傚嚭鏉ョ殑杈撳嚭灞傦級 %
    tanhLayer('name','softmax')
    regressionLayer('name','output')];

lgraph = layerGraph(layers)
lgraph = connectLayers(lgraph,'fold/miniBatchSize','unfold/miniBatchSize');





opts = trainingOptions('adam', ...
    'MaxEpochs',500, ...
    'GradientThreshold',1,...
    'ExecutionEnvironment','cpu',...
    'InitialLearnRate',InitialLearnRate, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropPeriod',100, ...   %
    'LearnRateDropFactor',0.5, ...
    'Shuffle','once',...  % 
    'SequenceLength',1,...
    'MiniBatchSize',128,...
    'Verbose',1,...
    'Plots','training-progress');


net = trainNetwork(Train_xNorm,Train_yNorm,lgraph,opts );




Predict_Ynorm_Train = net.predict(Train_xNorm);
Predict_Y_Train  = mapminmax('reverse',Predict_Ynorm_Train',yopt);
Predict_Y_Train = Predict_Y_Train';

figure
hold on 
plot(Predict_Y_Train,'r-','LineWidth',2.0)
plot(Train_y,'b-','LineWidth',2.0);    
ylabel('kW')
legend('预测值?','实际值')
title('训练')





Predict_Ynorm = net.predict(Test_xNorm);
Predict_Y  = mapminmax('reverse',Predict_Ynorm',yopt);
Predict_Y = Predict_Y';


figure
hold on 
plot(Predict_Y,'r-','LineWidth',2.0)
plot(Test_y,'b-','LineWidth',2.0)
legend('预测值','实际值')
ylabel('kW')
title('测试')



ae= abs(Predict_Y - Test_y);
rmse = (mean(ae.^2)).^0.5;
mse = mean(ae.^2);
mae = mean(ae);
mape = mean(ae./Predict_Y);

disp('误差评价指标?')
disp(['RMSE = ', num2str(rmse)])
disp(['MSE  = ', num2str(mse)])
disp(['MAE  = ', num2str(mae)])
disp(['MAPE = ', num2str(mape)])



数据

机器学习之MATLAB代码--基于MISO的SSA-CNN-LSTM(八)_第2张图片

结果

机器学习之MATLAB代码--基于MISO的SSA-CNN-LSTM(八)_第3张图片
机器学习之MATLAB代码--基于MISO的SSA-CNN-LSTM(八)_第4张图片
机器学习之MATLAB代码--基于MISO的SSA-CNN-LSTM(八)_第5张图片
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