DocumentCode :
3617508
Title :
Visual comparison of performance for different activation functions in MLP networks
Author :
F. Piekniewski;L. Rybicki
Author_Institution :
Fac. of Math. & Comput. Sci., Nicolaus Copernicus Univ., Torun, Poland
Volume :
4
fYear :
2004
fDate :
6/26/1905 12:00:00 AM
Firstpage :
2947
Abstract :
Multi layer perceptron networks have been successful in many applications, yet there are many unsolved problems in the theory. Commonly, sigmoidal activation functions have been used, giving good results. The backpropagation algorithm might work with any other activation function on one condition though - it has to have a differential. We investigate some possible activation functions and compare the results they give on some sample data sets.
Keywords :
"Intelligent networks","Transfer functions","Neurons","Neural networks","Mathematics","Computer science","Electronic mail","Logistics","Application software","Interpolation"
Publisher :
ieee
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-8359-1
Type :
conf
DOI :
10.1109/IJCNN.2004.1381133
Filename :
1381133
Link To Document :
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