How to minimaze the number of neural network input?
2 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
Gino Massafra
am 2 Okt. 2014
Beantwortet: Gino Massafra
am 6 Okt. 2014
Hi everybody, I am trying to choose which are the best input parameters for my feedforward neural network. Actually, I have 14 input parameters that give me good results in performance. However I've noticed that by using only 7 of them (choosen on the basis of my intuition), the performance increases. Is there therefore a way to know which are the best input parameters to increase the performance (probably reducing also the number of input parameters)? How to do it? Thanks
0 Kommentare
Akzeptierte Antwort
Greg Heath
am 5 Okt. 2014
Not enough details.
Finding the optimal combination is, in general, complicated. However, using stepwise or stepwisefit on a model that is linear in the variables generally yields a decent approximation.
In addition, if you start with a model that is quadratic in variables with squares and crossproducts but LINEAR IN COEFFICIENTS, the stepwise functions can still be used.
Otherwise, use a backward search with the NN inputs. For example
1. Train with all
2. Obtain the responses when each input is separately randomized.
3. Remove the variable who randomization degrades the performance the least
4. Repeat 2&3 until you can't stand further decreases in performance.
Hope this helps.
Thank you for formally accepting my answer
Greg
0 Kommentare
Weitere Antworten (1)
Siehe auch
Kategorien
Mehr zu Function Approximation and Clustering finden Sie in Help Center und File Exchange
Produkte
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!