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» Introduction to artificial neural networks
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ICANN
2009
Springer
14 years 2 months ago
Evolving Memory Cell Structures for Sequence Learning
The best recent supervised sequence learning methods use gradient descent to train networks of miniature nets called memory cells. The most popular cell structure seems somewhat ar...
Justin Bayer, Daan Wierstra, Julian Togelius, J&uu...
ESANN
2006
13 years 9 months ago
Magnification control for batch neural gas
Neural gas (NG) constitutes a very robust clustering algorithm which can be derived as stochastic gradient descent from a cost function closely connected to the quantization error...
Barbara Hammer, Alexander Hasenfuss, Thomas Villma...
ESANN
2006
13 years 9 months ago
Dynamical reservoir properties as network effects
It has been proposed that chaos can serve as a reservoir providing an infinite number of dynamical states [1, 2, 3, 4, 5]. These can be interpreted as different behaviors, search a...
Carlos Lourenço
ECAL
2007
Springer
14 years 2 months ago
The Evolution of Pain
We describe two simple simulations in which artificial organisms evolve an ability to respond to inputs from within their own body and these inputs themselves can evolve. In the fi...
Alberto Acerbi, Domenico Parisi
CEC
2005
IEEE
14 years 1 months ago
Genetic programming approach for fault modeling of electronic hardware
This paper presents two variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of el...
Ajith Abraham, Crina Grosan