DocumentCode
2957502
Title
The rule-extraction through the preimage analysis
Author
Tsaih, Rua-Huan ; Wan, Yat-Wah ; Huang, Shin-Ying
Author_Institution
Dept. of Manage. Inf. Syst., Nat. Chengchi Univ., Taipei
fYear
2008
fDate
1-8 June 2008
Firstpage
1488
Lastpage
1494
Abstract
This study reveals the properties of the input/output relationship for a real-valued single-hidden layer feed-forward neural network (SLFN) with the tanh activation function on all hidden-layer nodes and the linear activation function on output node. Specifically, the rule-extraction of the SLFN is done through mathematically analyzing its preimage, which is the set of input values for a given output value.
Keywords
feature extraction; feedforward neural nets; hidden-layer nodes; input-output relationship; linear activation function; preimage analysis; real-valued single-hidden layer feed-forward neural network; rule-extraction through; tanh activation function; Feedforward neural networks; Feedforward systems; Neural networks; Vectors; preimage; preimage analysis; single-hidden layer feed-forward neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location
Hong Kong
ISSN
1098-7576
Print_ISBN
978-1-4244-1820-6
Electronic_ISBN
1098-7576
Type
conf
DOI
10.1109/IJCNN.2008.4633993
Filename
4633993
Link To Document