DocumentCode :
2752955
Title :
Implementation of neural network with approximations functions
Author :
Hnatiuc, M. ; Lamarque, G.
Volume :
2
fYear :
2003
fDate :
0-0 2003
Firstpage :
553
Abstract :
The purpose of this work is to stimulate a neural network with non-linear activation functions. The non-linear functions are simulated in Microsoft Visual Studio C++ 6.0 to observe the precision and to implement on the programmable logic devices. This network is realized to accept very small input values. The multiplication between input values and weight values is realized with the add-logarithm and exponential functions. One approximates all the non-linear functions with linear functions using shift-add blocks.
Keywords :
approximation theory; neural nets; programmable logic devices; Gauss function; add-logarithm; approximation functions; exponential functions; linear functions; neural network; nonlinear activation functions; programmable logic devices; shift-add blocks; sigmoid function;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signals, Circuits and Systems, 2003. SCS 2003. International Symposium on
Print_ISBN :
0-7803-7979-9
Type :
conf
DOI :
10.1109/SCS.2003.1227112
Filename :
5731345
Link To Document :
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