【回归预测】基于萤火虫算法优化模糊实现数据回归预测附matlab代码.zip

%% Firefly Fuzzy Regression Algorithm - Created in (9 Jan 2022). % Firefly algorithm is one of the most decent algorithms in optimization % which could be used for various tasks and biasing weights. It is % relatively faster than others just like DE algorithm. So, there was no a % proper evolutionary linear regression Matlab code available in the web % and I decided to make one. You can use your data. System uses fuzzy logic % to create initial model and biases weights into a fit model by Firefly algorithm. You can play % with parameters (higher values, more calculation time). %% Well, Lets do it !!! clc; clear; warning('off'); % Data Loading data=JustLoad(); % Generate Fuzzy Model ClusNum=4; % Number of Clusters in FCM % fis=GenerateFuzzy(data,ClusNum); % %% Tarining FireFly Algorithm FireFlyFis=FireFlyRegression(fis,data); %% Plot Fuzzy FireFly Results (Train - Test) % Train Output Extraction TrTar=data.TrainTargets; TrainOutputs=evalfis(data.TrainInputs,FireFlyFis); % Test Output Extraction TsTar=data.TestTargets; TestOutputs=evalfis(data.TestInputs,FireFlyFis); % Train calc Errors=data.TrainTargets-TrainOutputs; MSE=mean(Errors.^2);RMSE=sqrt(MSE); error_mean=mean(Errors);error_std=std(Errors); % Test calc Errors1=data.TestTargets-TestOutputs; MSE1=mean(Errors1.^2);RMSE1=sqrt(MSE1); error_mean1=mean(Errors1);error_std1=std(Errors1); % Train figure('units','normalized','outerposition',[0 0 1 1]) subplot(3,2,1); plot(data.TrainTargets,'c');hold on; plot(TrainOutputs,'k');legend('Target','Output'); title('FireFly Training Part');xlabel('Sample Index');grid on; % Test subplot(3,2,2); plot(data.TestTargets,'c');hold on; plot(TestOutputs,'k');legend('FireFly Target','FireFly Output'); title('FireFly Testing Part');xlabel('Sample Index');grid on; % Train subplot(3,2,3); plot(Errors,'k');legend('FireFly Training Error'); title(['Train MSE = ' num2str(MSE) ' , Train RMSE = ' num2str(RMSE)]);grid on; % Test subplot(3,2,4); plot(Errors1,'k');legend('FireFly Testing Error'); title(['Test MSE = ' num2str(MSE1) ' , Test RMSE = ' num2str(RMSE1)]);grid on; % Train subplot(3,2,5); h=histfit(Errors, 50);h(1).FaceColor = [.8 .8 0.3]; title(['Train Error Mean = ' num2str(error_mean) ' , Train Error STD = ' num2str(error_std)]); % Test subplot(3,2,6); h=histfit(Errors1, 50);h(1).FaceColor = [.8 .8 0.3]; title(['Test Error Mean = ' num2str(error_mean1) ' , Test Error STD = ' num2str(error_std1)]); %% Plot Just Fuzzy Results (Train - Test) % Train Output Extraction fTrainOutputs=evalfis(data.TrainInputs,fis); % Test Output Extraction fTestOutputs=evalfis(data.TestInputs,fis); % Train calc fErrors=data.TrainTargets-fTrainOutputs; fMSE=mean(fErrors.^2);fRMSE=sqrt(fMSE); ferror_mean=mean(fErrors);ferror_std=std(fErrors); % Test calc fErrors1=data.TestTargets-fTestOutputs; fMSE1=mean(fErrors1.^2);fRMSE1=sqrt(fMSE1); ferror_mean1=mean(fErrors1);ferror_std1=std(fErrors1); % Train figure('units','normalized','outerposition',[0 0 1 1]) subplot(3,2,1); plot(data.TrainTargets,'m');hold on; plot(fTrainOutputs,'k');legend('Target','Output'); title('Fuzzy Training Part');xlabel('Sample Index');grid on; % Test subplot(3,2,2); plot(data.TestTargets,'m');hold on; plot(fTestOutputs,'k');legend('Target','Output'); title('Fuzzy Testing Part');xlabel('Sample Index');grid on; % Train subplot(3,2,3); plot(fErrors,'g');legend('Fuzzy Training Error'); title(['Train MSE = ' num2str(fMSE) ' , Test RMSE = ' num2str(fRMSE)]);grid on; % Test subplot(3,2,4); plot(fErrors1,'g');legend('Fuzzy Testing Error'); title(['Train MSE = ' num2str(fMSE1) ' , Test RMSE = ' num2str(fRMSE1)]);grid on; % Train subplot(3,2,5); h=histfit(fErrors, 50);h(1).FaceColor = [.3 .8 0.3]; title(['Train Error Mean = ' num2str(ferror_mean) ' , Train Error STD = ' num2str(ferror_std)]); % Test subplot(3,2,6); h=histfit(fErrors1, 50);h(1).FaceColor = [.3 .8 0.3]; title(['Test Error Mean = ' num2str(ferror_mean1) ' , Test Error STD = ' num2str(ferror_std1)]); %% Regression Plots figure('units','normalized','outerposition',[0 0 1 1]) subplot(2,2,1) [population2,gof] = fit(TrTar,TrainOutputs,'poly4'); plot(TrTar,TrainOutputs,'o',... 'LineWidth',1,... 'MarkerSize',6,... 'MarkerEdgeColor','g',... 'MarkerFaceColor',[0.9,0.1,0.1]); title(['FireFly Train - R = ' num2str(1-gof.rmse)]); xlabel('Train Target'); ylabel('Train Output'); hold on plot(population2,'b-','predobs'); xlabel('Train Target'); ylabel('Train Output'); hold off subplot(2,2,2) [population2,gof] = fit(TsTar, TestOutputs,'poly4'); plot(TsTar, TestOutputs,'o',... 'LineWidth',1,... 'MarkerSize',6,... 'MarkerEdgeColor','g',... 'MarkerFaceColor',[0.9,0.1,0.1]); title(['FireFly Test - R = ' num2str(1-gof.rmse)]); xlabel('Test Target'); ylabel('Test Output'); hold on plot(population2,'b-','predobs'); xlabel('Test Target'); ylabel('Test Output'); hold off subplot(2,2,3) [population2,gof] = fit(TrTar,fTrainOutputs,'poly4'); plot(TrTar,fTrainOutputs,'o',... 'LineWidth',1,... 'MarkerSize',6,... 'MarkerEdgeColor','b',... 'MarkerFaceColor',[0.3,0.9,0.2]); title(['Fuzzy Train - R = ' num2str(1-gof.rmse)]); xlabel('Train Target'); ylabel('Train Output'); hold on plot(population2,'r-','predobs'); xlabel('Train Target'); ylabel('Train Output'); hold off subplot(2,2,4) [population2,gof] = fit(TsTar, fTestOutputs,'poly4'); plot(TsTar, fTestOutputs,'o',... 'LineWidth',1,... 'MarkerSize',6,... 'MarkerEdgeColor','b',... 'MarkerFaceColor',[0.3,0.9,0.2]); title(['Fuzzy Test - R = ' num2str(1-gof.rmse)]); xlabel('Test Target'); ylabel('Test Output'); hold on plot(population2,'r-','predobs'); xlabel('Test Target'); ylabel('Test Output'); hold off %% Errors % Fuzzy Regression Train and Test Errors] % Train fprintf('Fuzzy Regression Training "MSE" Is = %0.4f.\n',fMSE) fprintf('Fuzzy Regression Training "RMSE" Is = %0.4f.\n',fRMSE) fprintf('Fuzzy Regression Training "Mean Error" Is = %0.4f.\n',ferror_mean) fprintf('Fuzzy Regression Training "STD Error" Is = %0.4f.\n',ferror_std) fprintf('Fuzzy Regression Training "MAE" Is = %0.4f.\n',mae(data.TrainTargets,fTrainOutputs)) % Test fprintf('Fuzzy Regression Testing "MSE" Is = %0.4f.\n',fMSE1) fprintf('Fuzzy Regression Testing "RMSE" Is = %0.4f.\n',fRMSE1) fprintf('Fuzzy Regression Testing "Mean Error" Is = %0.4f.\n',ferror_mean1) fprintf('Fuzzy Regression Testing "STD Error" Is = %0.4f.\n',ferror_std1) fprintf('Fuzzy Regression Testing "MAE" Is = %0.4f.\n',mae(data.TestTargets,fTestOutputs)) % FireFly Regression Algorithm Train and Test Errors % Train fprintf('FireFly Regression Training "MSE" Is = %0.4f.\n',MSE) fprintf('FireFly Regression Training "RMSE" Is = %0.4f.\n',RMSE) fprintf('FireFly Regression Training "Mean Error" Is = %0.4f.\n',error_mean) fprintf('FireFly Regression Training "STD Error" Is = %0.4f.\n',error_std) fprintf('FireFly Regression Training "MAE" Is = %0.4f.\n',mae(data.TrainTargets,TrainOutputs)) % Test fprintf('FireFly Regression Testing "MSE" Is = %0.4f.\n',MSE1) fprintf('FireFly Regression Testing "RMSE" Is = %0.4f.\n',RMSE1) fprintf('FireFly Regression Testing "Mean Error" Is = %0.4f.\n',error_mean1) fprintf('FireFly Regression Testing "STD Error" Is = %0.4f.\n',error_std1) fprintf('FireFly Regression Testing "MAE" Is = %0.4f.\n',mae(data.TestTargets,TestOutputs))