DocumentCode
2617813
Title
Capabilities of a three layer feedforward neural network
Author
Tamura, Shin´ichi
Author_Institution
Sony Co., Tokyo, Japan
fYear
1991
fDate
18-21 Nov 1991
Firstpage
2757
Abstract
Mapping capabilities of a three-layer feedforward neural network with a finite number of hidden units which have sigmoid functions as their nonlinearities are discussed. It is proved that sigmoid functions of a hidden layer of the network can raise the dimension of the input space up to the number of the hidden units. From this result, it is concluded that a three-layer feedforward neural network with N hidden units can assign arbitrary analog values to N arbitrary input vectors
Keywords
neural nets; hidden units; input vectors; mapping; nonlinearities; sigmoid functions; three layer feedforward neural network; Bismuth; Ear; Expert systems; Feedforward neural networks; Fourier transforms; Humans; Image coding; Neural networks; Speech recognition; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN
0-7803-0227-3
Type
conf
DOI
10.1109/IJCNN.1991.170332
Filename
170332
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