mr.mo asked . 2021-07-28

What kind of RMSE should I choose to show the capability

What kind of RMSE should I choose to show the capability of function approximation with Neural Networks ? 

 -0.29039678  -0.212428117
  0.149798734  0.268486256
  0.851351352  0.563358782
  -0.694364578  -0.652148947
  -0.326624496  -0.468058466
  -0.019838988  0.209298441
  -0.765669926  -0.828415796
  -0.133410006  0.003000269
  -0.122484186  -0.073538329
  -0.471535366  -0.376903371
  0.181426106  0.365548334
  0.186889016  0.37947765
  -0.260782058  -0.024155069
  -0.215353652  -0.371639751
  -0.062967222  0.092607096
  -0.461759632  -0.56834877
  0.074468086  0.154155435
  0.624496838  0.529777368
  -0.197814836  -0.096543508
  -0.247268546  -0.136442585
  0.106382978  0.28668492
  -0.791259344  -0.718599416
  -0.532489936  -0.572352826
  -0.006325474  -0.010056861
  0.48706153  0.485766679
  -0.631397354  -0.619362709
  -0.815698678  -0.823629139
  0.272857964  0.354840024
  0.080506038  0.220713202

lm=fitlm(x,y,'linear');

or RMSE obtained by the formula shown below?

RMSE = sqrt(sum((data(:) - estimate (:)).^2) / numel(data));

    lm.predict(x)

and my Output vector are different.

rmse , regression , neural networks , fitlm