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» On Generalization by Neural Networks
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EAAI
2006
123views more  EAAI 2006»
15 years 4 months ago
Imitation learning with spiking neural networks and real-world devices
This article is about a new approach in robotic learning systems. It provides a method to use a real-world device that operates in real-time, controlled through a simulated recurr...
Harald Burgsteiner
GECCO
2009
Springer
199views Optimization» more  GECCO 2009»
15 years 8 months ago
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution
Encouraging exploration, typically by preserving the diversity within the population, is one of the most common method to improve the behavior of evolutionary algorithms with dece...
Jean-Baptiste Mouret, Stéphane Doncieux
AINA
2010
IEEE
15 years 3 months ago
Compensation of Sensors Nonlinearity with Neural Networks
—This paper describes a method of linearizing the nonlinear characteristics of many sensors using an embedded neural network. The proposed method allows for complex neural networ...
Nicholas J. Cotton, Bogdan M. Wilamowski
179
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NABIC
2010
14 years 10 months ago
Predictive internal neural dynamics for delay compensation
Neural transmission delay may cause serious problems unless a compensation mechanism exists in the neural system. We showed previously that facilitating neural dynamics is a key me...
Jaerock Kwon, Yoonsuck Choe
SAB
2010
Springer
117views Optimization» more  SAB 2010»
15 years 2 months ago
Indirectly Encoding Neural Plasticity as a Pattern of Local Rules
Biological brains can adapt and learn from past experience. In neuroevolution, i.e. evolving artificial neural networks (ANNs), one way that agents controlled by ANNs can evolve t...
Sebastian Risi, Kenneth O. Stanley