Taxonomy of neural transfer functions

Author

Duch, Wtodzistaw ; Jankowski, Norbert

Author_Institution

Dept. of Comput. Methods, Nicholas Copernicus Univ., Torun, Poland

Volume

3

fYear

2000

fDate

2000

Firstpage

477

Abstract

The choice of transfer functions may strongly influence complexity and performance of neural networks used in classification and approximation tasks. A taxonomy of activation and output functions is proposed, allowing to generate many transfer functions. Several less-known types of transfer functions and new combinations of activation/output functions are described. Functions parameterize to change from localized to delocalized type, functions with activation based on nonEuclidean distance measures, bicentral functions formed from pairs of sigmoids are discussed

Keywords

approximation theory; computational complexity; neural nets; pattern classification; transfer functions; activation functions; activation/output function combinations; approximation; bicentral functions; neural network complexity; neural network performance; neural transfer function taxonomy; non-Euclidean distance measures; nonEuclidean distance measures; pattern classification; sigmoid pairs; Adaptive systems; Approximation methods; Multilayer perceptrons; Neural networks; Neurons; Statistical analysis; Taxonomy; Transfer functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Conference_Location

Como

ISSN

1098-7576

Print_ISBN

0-7695-0619-4

Type

conf

DOI

10.1109/IJCNN.2000.861353

Filename

861353