% entering input data i.e., fabric property thickness x = [ 0.37 0.41 0.48 0.49 0.47 0.37 0.41 0.48 0.49 0.47 0.37 0.41 0.48 0.49 0.47 0.37 0.41 0.48 0.49 0.47 0.37 0.41 0.48 0.49 0.47 0.37 0.41 0.48 0.49 0.47]; % entering output data i.e., protective performance HTP t = [ 5.97 7.01 7.48 6.39 7.61 20 20 11.62 6.08 11.5 20 20 10.16 6.9 15.32 13.14 15.47 8.42 13.71 7.87 20 20 13.07 13.88 12.73 20 20 17.39 13.96 18.52]; % Choose a Training Function trainFcn = ‘trainlm’; % Levenberg-Marquardt backpropagation. ‘trainlm’ is usually fastest. % Create a Fitting Network hiddenLayerSize = 1;% number of hidden neurons. nethtp = fitnet (hiddenLayerSize,trainFcn); % Setup Division of Data for Training, Validation, Testing nethtp.divideParam.trainRatio = 70/100; nethtp.divideParam.valRatio = 15/100; nethtp.divideParam.testRatio = 15/100; nethtp.trainParam.epochs=3000;% number of training epochs. % Train the Network [nethtp,tr] = train (nethtp,x,t); % Test the Network y = nethtp (x); e = gsubtract (t,y); performance = perform (nethtp,t,y) % View the Network view (nethtp) % calculating the root mean square error rmse=sqrt (performance); % Plots figure, plotperform (tr) figure, plottrainstate (tr) figure, ploterrhist (e) figure, plotregression (t,y) % using the regression analysis to judge the network performance [m,b,r]=postreg (y,t); % saving the trained network save nethtp;