About the value of spread of GRNN

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Hi, everyone! I encountered some problem when using GRNN in neural network tool box. Spread is the only variable need to be determined in GRNN. I have tried different values for spread, the curve fitting capability for training data is good, but the interpolation for regions where training data is unavailiable is very poor. How can I improve the interpolation capability by using GRNN? Thanks!

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Greg Heath am 10 Aug. 2013

Not enough information:

size(input)?

size(target)?

How are input and target scaled?

What range of spread did you use?

huan tu am 11 Aug. 2013

In MATLAB Online öffnen

    clc,clear
    % samples
    data=[  550  50.9114   4.9195 
            550  53.9864   4.8926 
            550  56.3262   4.8658 
            550  58.5372   4.5436 
            550  59.9610   3.3356 
            500  42.2350   4.1678 
            500  47.3824   4.1141 
            500  49.3885   4.0604 
            500  52.0637   3.9597 
            500  54.0059   3.7248 
            500  54.6201   2.9664 
            475  38.7662   3.7584 
            475  43.1781   3.7248 
            475  45.1173   3.6711 
            475  47.5921   3.5638 
            475  50.0018   3.3557 
            475  50.6136   2.7383 
            450  34.8298   3.3423  
            450  37.0355   3.3356 
            450  40.7792   3.2886 
            450  43.9895   3.1678 
            450  45.7969   3.0000 
            450  46.3401   2.4899 
            400  28.0909   2.6476
            400  31.3660   2.6376 
            400  33.3715   2.6107 
            400  35.9125   2.5436 
            400  37.9872   2.3826 
            400  38.5303   1.8792 
            350  19.5438   2.1477 
            350  24.2224   2.1544 
            350  27.0301   2.1208 
            350  29.2368   2.0604 
            350  31.1102   1.9463 
            350  31.7844   1.5973 
            300  14.3362   1.8054 
            300  19.3493   1.7852 
            300  21.4217   1.7651 
            300  23.4944   1.7181 
            300  25.2336   1.6376 
            300  26.5071   1.4228 ];
      data=[data(:,1) data(:,3) data(:,2)];   
      p=data(:,[1 3])';
      t=data(:,2)';
        % interpolation validation
        data1=   [525.0000       46.3723
                  525.0000       50.6499
                  525.0000       52.6551
                  525.0000       53.6585
                  525.0000       54.0599
                  525.0000       55.1983
                  525.0000       56.7400
                  525.0000       57.6911];
         dataInterp=data1';
    [normInput,ps]=mapminmax(p);
    [normTarget,ts]=mapminmax(t);
    normInterp=mapminmax('apply',dataInterp,ps);
    spread=0.01;
    net=newgrnn(normInput,normTarget,spread);
    normTrainOutput=sim(net,normInput);
    normInterpOutput=sim(net,normInterp);
    TrainOutput=mapminmax('reverse',normTrainOutput,ts);
    InterpOutput=mapminmax('reverse',normInterpOutput,ts);
    figure(1)
    % plot predictions at samples
    plot(p(2,:),TrainOutput,'o',...
         p(2,:),t,'r*',...
         dataInterp(2,:),InterpOutput,'o')
    hold on 
    % plot interpolation
    plot(dataInterp(2,:),InterpOutput,'o')
       refInterp=[525.0000    4.5638   46.3723
                  525.0000    4.5705   50.6499
                  525.0000    4.5638   52.6551
                  525.0000    4.5168   53.6585
                  525.0000    4.4899   54.0599
                  525.0000    4.3624   55.1983
                  525.0000    4.1007   56.7400
                  525.0000    3.1812   57.6911]';
    plot(refInterp(3,:),refInterp(2,:),'rd','MarkerFaceColor','r')