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src/Untitled-1.m
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src/Untitled-1.m
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%% Set up the Import Options and import the data
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opts = delimitedTextImportOptions("NumVariables", 8);
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% Specify range and delimiter
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opts.DataLines = [1, Inf];
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opts.Delimiter = ",";
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% Specify column names and types
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opts.VariableNames = ["Iteration", "NoseRadius", "CuttingSpeed", "FeedRate", "DepthOfCut", "ForceRatio", "AverageSurfaceRoughness", "MaximumSurfaceRoughness"];
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opts.VariableTypes = ["double", "double", "double", "double", "double", "double", "double", "double"];
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% Specify file level properties
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opts.ExtraColumnsRule = "ignore";
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opts.EmptyLineRule = "read";
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% Import the data
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Data = readtable("D:\Nextcloud\siwat\Documents\Lean Manufacturing\Data.csv", opts);
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%% Clear temporary variables
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clear opts
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% Remove Header
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Data = Data(2:end,:);
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inputs = [Data.NoseRadius,Data.CuttingSpeed,Data.FeedRate, Data.DepthOfCut];
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outputs = [Data.ForceRatio, Data.AverageSurfaceRoughness, Data.MaximumSurfaceRoughness];
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% Solve an Input-Output Fitting problem with a Neural Network
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% Script generated by Neural Fitting app
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% Created 19-Mar-2024 12:05:08
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%
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% This script assumes these variables are defined:
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%
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% inputs - input data.
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% outputs - target data.
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x = inputs';
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t = outputs';
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% Choose a Training Function
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% For a list of all training functions type: help nntrain
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% 'trainlm' is usually fastest.
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% 'trainbr' takes longer but may be better for challenging problems.
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% 'trainscg' uses less memory. Suitable in low memory situations.
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trainFcn = 'trainlm'; % Levenberg-Marquardt backpropagation.
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% Create a Fitting Network
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hiddenLayerSize = 50;
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net = fitnet(hiddenLayerSize,trainFcn);
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% Choose Input and Output Pre/Post-Processing Functions
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% For a list of all processing functions type: help nnprocess
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net.input.processFcns = {'removeconstantrows','mapminmax'};
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net.output.processFcns = {'removeconstantrows','mapminmax'};
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% Setup Division of Data for Training, Validation, Testing
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% For a list of all data division functions type: help nndivision
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net.divideFcn = 'dividerand'; % Divide data randomly
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net.divideMode = 'sample'; % Divide up every sample
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net.divideParam.trainRatio = 70/100;
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net.divideParam.valRatio = 15/100;
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net.divideParam.testRatio = 15/100;
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% Choose a Performance Function
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% For a list of all performance functions type: help nnperformance
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net.performFcn = 'mse'; % Mean Squared Error
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% Choose Plot Functions
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% For a list of all plot functions type: help nnplot
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net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
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'plotregression', 'plotfit'};
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% Train the Network
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[net,tr] = train(net,x,t);
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% Test the Network
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y = net(x);
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e = gsubtract(t,y);
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performance = perform(net,t,y)
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% Recalculate Training, Validation and Test Performance
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trainTargets = t .* tr.trainMask{1};
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valTargets = t .* tr.valMask{1};
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testTargets = t .* tr.testMask{1};
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trainPerformance = perform(net,trainTargets,y)
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valPerformance = perform(net,valTargets,y)
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testPerformance = perform(net,testTargets,y)
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% View the Network
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view(net)
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% Plots
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% Uncomment these lines to enable various plots.
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%figure, plotperform(tr)
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%figure, plottrainstate(tr)
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%figure, ploterrhist(e)
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%figure, plotregression(t,y)
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%figure, plotfit(net,x,t)
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% Deployment
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% Change the (false) values to (true) to enable the following code blocks.
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% See the help for each generation function for more information.
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if (false)
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% Generate MATLAB function for neural network for application
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% deployment in MATLAB scripts or with MATLAB Compiler and Builder
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% tools, or simply to examine the calculations your trained neural
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% network performs.
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genFunction(net,'myNeuralNetworkFunction');
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y = myNeuralNetworkFunction(x);
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end
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if (false)
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% Generate a matrix-only MATLAB function for neural network code
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% generation with MATLAB Coder tools.
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genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes');
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y = myNeuralNetworkFunction(x);
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end
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if (false)
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% Generate a Simulink diagram for simulation or deployment with.
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% Simulink Coder tools.
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gensim(net);
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end
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%% Test Model with Data
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% Sample Data
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NoseRadius = [0.4 0.4 0.8 0.8 0.8];
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CuttingSpeed = [250 250 250 250 250];
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FeedRate = [0.18 0.18 0.18 0.18 0.18];
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DepthOfCut = [0.25 0.25 0.25 0.25 0.25];
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% Expected Data
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ForceRatio = [0.324,0.281,0.256,0.26,0.181];
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SurfaceRoughness = [2.557 2.773 1.827 2.403 2.987];
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MaximumSurfaceRoughness = [12.387 13.478 9.110 13.878 18.304];
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% Test Model for error
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prediction = [];
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for i = 1:5
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test = [NoseRadius(i),CuttingSpeed(i),FeedRate(i),DepthOfCut(i)];
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test = test';
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y = net(test);
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% Print Expected and Predicted Value
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fprintf('Test %d\n',i);
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fprintf('Nose Radius: %f\n',NoseRadius(i));
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fprintf('Cutting Speed: %f\n',CuttingSpeed(i));
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fprintf('Feed Rate: %f\n',FeedRate(i));
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fprintf('Depth of Cut: %f\n',DepthOfCut(i));
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fprintf('Predicted Force Ratio: %f\n',y(1));
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fprintf('Expected Force Ratio: %f\n',ForceRatio(i));
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fprintf('Error Force Ratio: %f\n',abs(ForceRatio(i)-y(1)));
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fprintf('Predicted Surface Roughness: %f\n',y(2));
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fprintf('Expected Surface Roughness: %f\n',SurfaceRoughness(i));
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fprintf('Error Surface Roughness: %f\n',abs(SurfaceRoughness(i)-y(2)));
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fprintf('Predicted Maximum Surface Roughness: %f\n',y(3));
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fprintf('Expected Maximum Surface Roughness: %f\n',MaximumSurfaceRoughness(i));
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fprintf('Error Maximum Surface Roughness: %f\n',abs(MaximumSurfaceRoughness(i)-y(3)));
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fprintf('\n');
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prediction = [prediction,y];
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end
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prediction = prediction';
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% Plot graph comparing the expected and predicted values
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figure;
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subplot(3,1,1);
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plot(1:5,ForceRatio,'-o',1:5,prediction(:,1),'-o');
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title('Force Ratio');
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xlabel('Test');
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ylabel('Force Ratio');
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legend('Expected','Predicted');
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subplot(3,1,2);
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plot(1:5,SurfaceRoughness,'-o',1:5,prediction(:,2),'-o');
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title('Surface Roughness');
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xlabel('Test');
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ylabel('Surface Roughness');
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legend('Expected','Predicted');
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subplot(3,1,3);
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plot(1:5,MaximumSurfaceRoughness,'-o',1:5,prediction(:,3),'-o');
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title('Maximum Surface Roughness');
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xlabel('Test');
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ylabel('Maximum Surface Roughness');
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legend('Expected','Predicted');
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