% entering the input data i.e., thickness x = [ 0.37 0.41 0.48 0.49 0.47 0.37 0.41 0.48 0.49 0.47]; % entering the output data i.e., THL t = [542.79 511.79 797.09 628.68 764.79 237.8 229.5 371.7 269.87 266]; % Choose a Training Function trainFcn = ‘trainlm’; % Levenberg-Marquardt backpropagation. ‘trainlm’ is usually fastest. % Create a Fitting Network hiddenLayerSize = 3;% number of hidden neurons. netthl = fitnet (hiddenLayerSize,trainFcn); % Setup Division of Data for Training, Validation, Testing netthl.divideParam.trainRatio = 70/100; netthl.divideParam.valRatio = 15/100; netthl.divideParam.testRatio = 15/100; netthl.trainParam.epochs=3000;% number of training epochs. % Train the Network [netthl,tr] = train (netthl,x,t); % Test the Network y = netthl (x); e = gsubtract (t,y); performance = perform (netthl,t,y) % View the Network view (netthl) % 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 netthl;