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» Introduction to artificial neural networks
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GPEM
2006
82views more  GPEM 2006»
13 years 8 months ago
Shortcomings with using edge encodings to represent graph structures
There are various representations for encoding graph structures, such as artificial neural networks (ANNs) and circuits, each with its own strengths and weaknesses. Here we analyz...
Gregory Hornby
CEC
2007
IEEE
14 years 2 months ago
Evolving neuromodulatory topologies for reinforcement learning-like problems
— Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advanta...
Andrea Soltoggio, Peter Dürr, Claudio Mattius...
ECAL
2001
Springer
14 years 14 days ago
The Shifting Network: Volume Signalling in Real and Robot Nervous Systems
This paper presents recent work in computational modelling of diffusing gaseous neuromodulators in biological nervous systems. It goes on to describe work in adaptive autonomous sy...
Phil Husbands, Andrew Philippides, Tom Smith, Mich...
GI
1998
Springer
14 years 6 days ago
Self-Organizing Data Mining
"KnowledgeMiner" was designed to support the knowledge extraction process on a highly automated level. Implemented are 3 different GMDH-type self-organizing modeling algo...
Frank Lemke, Johann-Adolf Müller
ICIP
1994
IEEE
14 years 9 months ago
A Connectionist Model for Local Speed Estimation
Classical models for motion detection with artificial neural networks are inspired in physiological data of simple visual systems. Local speed estimationis a problem that involves...
Francisco J. Vico, F. J. Garrido, Francisco Sandov...