Hi! There are 55 sampels, 2 inputs and 1 output. The predictions at samples are exellent, but the interpolation are very poor. How can I improve the RBF NN for interpolaion? Thanks!
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 525 46.3723 4.5689 525 50.6499 4.5638 525 52.6551 4.5607 525 53.6585 4.5168 525 54.0599 4.4899 525 55.1983 4.3624 525 56.7400 4.1007 525 57.6911 3.1812 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 250 11.1324 1.5369 250 15.6110 1.5101 250 17.2824 1.4832 250 18.3522 1.4631 250 19.2214 1.4430 250 21.8308 1.2819 ]; p=data(:,[1 3])'; t=data(:,2)'; % input data for interpolation
data1=[ 425 2.3000 425 2.3500 425 2.4000 425 2.4500 425 2.5000 425 2.5500 425 2.6000 425 2.6500 425 2.7000 425 2.7500 425 2.8000 425 2.8500 425 2.9000 425 2.9500 425 3.0000 ]; inputInterp=data1';net=newrbe(p,t);outTrain=sim(net,p);% comparison of samples and predictions
plot(outTrain,p(2,:),'o',t,p(2,:),'r*')hold on% interpolation
% outInterp=sim(net,inputInterp);
% plot(outInterp,inputInterp(2,:),'o')
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