promideson asked . 2021-07-29

does anybody ever use neural network to do a prediction

            % Create a Fitting Network
            hiddenLayerSize = k;
            net = fitnet(hiddenLayerSize); % net = fitnet([hiddenLayerSize,nn]); nn is the number of hidden layer
            net=init(net); % initialize the network

            % Choose Input and Output Pre/Post-Processing Functions
            % For a list of all processing functions type: help nnprocess
            net.inputs{1}.processFcns = {'removeconstantrows'};
            net.outputs{2}.processFcns = {'removeconstantrows'};

            % Setup Division of Data for Training, Validation, Testing
            % For a list of all data division functions type: help nndivide
            net.divideFcn = 'divideind';  % Divide data by index
            net.divideParam.trainInd = trnind;
            net.divideParam.testInd = tstind;

            % set the transfer function (activation function) for input and
            % output layers
            net.layers{1}.transferFcn = 'tansig'; % layer 1 corresponds to the hidden layer
            net.layers{2}.transferFcn = 'purelin'; % layer 2 corresponds to the output layer

            % For help on training function 'trainlm' type: help trainlm
            % For a list of all training functions type: help nntrain
            net.trainFcn = 'trainbr';  % Levenberg-Marquardt optimization with Bayesian regularization
            net.trainParam.goal=0.01.*var(targets); % usually set this to be 1% of the var(target)
            net.trainParam.epochs=500;
            net.trainParam.mu_dec=0.8;
            net.trainParam.mu_inc=1.5;
            % Choose a Performance Function
            % For a list of all performance functions type: help nnperformance
            net.performFcn = 'msereg';  % Mean squared error

            % Choose Plot Functions
            % For a list of all plot functions type: help nnplot
            net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
              'plotregression', 'plotfit'};

            % Train the Network
            [net,tr] = train(net,inputs,targets);

            % Test the Network
            outputs = net(inputs);
            errors = gsubtract(targets,outputs);
            performance = perform(net,targets,outputs);

            % Recalculate Training and Test Performance
            trainTargets = targets .* tr.trainMask{1};
            testTargets = targets  .* tr.testMask{1};
            trainPerformance = perform(net,trainTargets,outputs);
            testPerformance = perform(net,testTargets,outputs);
            %some statistics codes, not shown here
end

neural network for..., mtlab , matlabsolutions