FAN: LEARNING BY MEANS OF FREE ASSOCIATIVE NEURONS
?. CONGRESS ON COMPUTATIONAL INTELLIGENCE, FUZZ-IEEE'98, (1998)
Abstract
In this paper we introduce a new learning method: Free
Associative Neurons (FAN). FAN is composed by
independent units with autonomous learning capability.
The learning power of FAN is based on the association its units and on the use of granularity for representing information. As a method for representing complex environments, FAN can be used in Pattern Recognition,
Classification and Diagnosis. This paper is focused on the
principles governing FAN. We also discuss some results
compared to well-known Artijicial Neural Network
algorithms.
%0 Journal Article
%1 FAN
%A ?,
%D 1998
%J CONGRESS ON COMPUTATIONAL INTELLIGENCE, FUZZ-IEEE'98
%K Fuzzy Hybrid Netowroks Networks Neural Systems
%T FAN: LEARNING BY MEANS OF FREE ASSOCIATIVE NEURONS
%U http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=624080&userType=inst
%X In this paper we introduce a new learning method: Free
Associative Neurons (FAN). FAN is composed by
independent units with autonomous learning capability.
The learning power of FAN is based on the association its units and on the use of granularity for representing information. As a method for representing complex environments, FAN can be used in Pattern Recognition,
Classification and Diagnosis. This paper is focused on the
principles governing FAN. We also discuss some results
compared to well-known Artijicial Neural Network
algorithms.
@article{FAN,
abstract = {In this paper we introduce a new learning method: Free
Associative Neurons (FAN). FAN is composed by
independent units with autonomous learning capability.
The learning power of FAN is based on the association its units and on the use of granularity for representing information. As a method for representing complex environments, FAN can be used in Pattern Recognition,
Classification and Diagnosis. This paper is focused on the
principles governing FAN. We also discuss some results
compared to well-known Artijicial Neural Network
algorithms.},
added-at = {2010-03-23T15:10:23.000+0100},
author = {?},
biburl = {https://www.bibsonomy.org/bibtex/228e25df0e4a91d6d7e5c7ba241b54727/dieval},
interhash = {0dcfc5a364bd29bb91398e5d738ca15b},
intrahash = {28e25df0e4a91d6d7e5c7ba241b54727},
journal = {CONGRESS ON COMPUTATIONAL INTELLIGENCE, FUZZ-IEEE'98},
keywords = {Fuzzy Hybrid Netowroks Networks Neural Systems},
timestamp = {2010-03-23T15:10:23.000+0100},
title = {FAN: LEARNING BY MEANS OF FREE ASSOCIATIVE NEURONS},
url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=624080&userType=inst},
year = 1998
}