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» Evolving Artificial Neural Networks that Develop in Time
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Publication
295views
13 years 10 months ago
The Age of Analog Networks.
A large class of systems of biological and technological relevance can be described as analog networks, that is, collections of dynamic devices interconnected by links of varying s...
Claudio Mattiussi, Daniel Marbach, Peter Dürr, Da...
IJCNN
2000
IEEE
15 years 3 months ago
Unsupervised Classification of Complex Clusters in Networks of Spiking Neurons
For unsupervised clustering in a network of spiking neurons we develop a temporal encoding of continuously valued data to obtain arbitrary clustering capacity and precision with a...
Sander M. Bohte, Johannes A. La Poutré, Joo...
GECCO
2003
Springer
100views Optimization» more  GECCO 2003»
15 years 5 months ago
Active Guidance for a Finless Rocket Using Neuroevolution
Finless rockets are more efficient than finned designs, but are too unstable to fly unassisted. These rockets require an active guidance system to control their orientation durin...
Faustino J. Gomez, Risto Miikkulainen
AI
2010
Springer
14 years 6 months ago
Robustness of Classifiers to Changing Environments
Abstract. In this paper, we test some of the most commonly used classifiers to identify which ones are the most robust to changing environments. The environment may change over tim...
Houman Abbasian, Chris Drummond, Nathalie Japkowic...
IJCNN
2008
IEEE
15 years 6 months ago
A neural wake-sleep learning architecture for associating robotic facial emotions
—A novel wake-sleep learning architecture for processing a robot’s facial expressions is introduced. According to neuroscience evidence, associative learning of emotional respo...
Chi-Yung Yau, Kevin Burn, Stefan Wermter