Newff vs patternet in my program
I have written a code , the purpose of the code is classification of EEG datasets related to emotion, I have used both newff and patternet , I have always thought there is no difference between them except for the training function they use , but the thing is there are tremendous changes between the results; for example some times when I use same number of neurons for my network hidden layer, using newff leads to some results while using patternet may lead to no result , it seems net is not working or the result will lead to a very big error. I wonder why is that for. more over I assume in new version of net codes I mean patternet for example there are some ways that prevent overfitting happens even we set net.divideParam.valRatio=0 , it seems there are some hidden strategies that keep net from overfitting. I would really appreciate it if some one helps me to figure these differences out.
Kshitij Singh answered .
2025-11-20
You are very confused. The current MLP functions are
-FITNET for regression and curve-fitting -PATTERNNET for classification and pattern-recognition -FEEDFORWARDNET a generic net called by FITNET and PATTERNNET
They replace the obsolete but still available functions
-NEWFIT for regression and curve-fitting -NEWPR for classification and pattern-recognition -NEWFF a generic net called by NEWFIT and NEWPR
help patternnet doc patternnet type patternnet