"Imran Babar" wrote in message <email@example.com>... > I am using for my problem trainlm and trainbr training algorithms for BPNN. I am getting good results but I am confused a little bit about results of trainbr. The results produced by trainbr algorithm are better optimized than trainlm. However I need verification by some expert that either these are ok or there is any problem. The results are shown below. > > Input: 32, 30, 34, 35, 36 > Target: 0.9900, 0.9500, 1.0300, 1.0500, 1.0700 > Trainlm: 0.9934, 0.9523, 1.0279, 1.0536, 1.0665 > Trainbr: 0.9901, 0.9501, 1.0299, 1.0501, 1.0699 > > These results are obtained for validation data. The results in case of trainbr are very close to the target data. However I just want to know that these results are ok or the neural network is not trained properly for trainbr. Advance thanks for any help and guidance.
Trainlm validation data is used to determine when to stop training. Therefore, it is considered part of the design data (Design = Training + Validation). Consequently, using the validation error rate to evaluate the net does not result in an unbiased estimate. Nonetheless, the validation error rate can be used to choose the "best" of multiple designs. However an unbiased evaluation must be obtained from a nondesign , test data set that is large enough to yield a robust estimate (e.g., the estimated standard deviation is much less than the estimated mean).
Trainbr does not use a validation set. Therefore, I do not really know what you are doing.
Unbiased, reasonably accurate evaluations require multiple designs using reasonable large data sets. See Wikipedia for theoretical estimates of standard deviation for chisquare (regression) error and binomial(classification) error.