How to achieve better regression using Neural network having 364*6 inputs and 364*1 output?
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Hi, I have 364*6 inputs and 364*1 output and loading into neural network of 10 neurons and all other settings are given below:
clear all; 
  close all;
load('nmtraindata.mat'); 
in=input';
ref=output';
    net = feedforwardnet(10);
    % net.performFcn = 'mse';
    net.performFcn = 'mae';
    %set NN function and other parameters 2if desired
          net.layers{1}.transferFcn = 'tansig'; % transfer function is "x=y" %tansig logsig
    % net.layers{2}.transferFcn = 'tansig';
          net.trainParam.max_fail = 1000; 
          net.trainParam.min_grad=1e-9; 
          net.trainParam.show=10; %showing intervals
          net.trainParam.lr=0.1; %learning rate
          net.trainParam.epochs=1000; %maximum iterations 
          net.trainParam.goal=0; %error goal
          net.trainParam.mc = 0.9;
    % train the NN
    net = train(net,in,ref); 
    estim_out = sim(net,in); % simulate the network to calculate output
    plot(estim_out,'r'); hold on;
    plot(ref,'b')
If there is less noisy data then regression plot is 1 (desired) but when i program large data set like the above one, i don't regression above 0.5. Am I missing something, like, do I need to change settings of neural network?
Looking forward for a positive solution and guidelines to this problem.


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