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» Explanation-Based Neural Network Learning for Robot Control
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134
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TSMC
2008
177views more  TSMC 2008»
15 years 8 days ago
Adaptive Critic Learning Techniques for Engine Torque and Air-Fuel Ratio Control
A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning con...
Derong Liu, Hossein Javaherian, Olesia Kovalenko, ...
101
Voted
ISCAS
2005
IEEE
154views Hardware» more  ISCAS 2005»
15 years 7 months ago
Back propagation learning of neural networks with chaotically-selected affordable neurons
— Cell assembly is one of explanations of information processing in the brain, in which an information is represented by a firing space pattern of a group of plural neurons. On ...
Yoko Uwate, Yoshifumi Nishio
IEAAIE
2005
Springer
15 years 7 months ago
Movement Prediction from Real-World Images Using a Liquid State Machine
Prediction is an important task in robot motor control where it is used to gain feedback for a controller. With such a self-generated feedback, which is available before sensor rea...
Harald Burgsteiner, Mark Kröll, Alexander Leo...
GECCO
2011
Springer
256views Optimization» more  GECCO 2011»
14 years 5 months ago
Evolving complete robots with CPPN-NEAT: the utility of recurrent connections
This paper extends prior work using Compositional Pattern Producing Networks (CPPNs) as a generative encoding for the purpose of simultaneously evolving robot morphology and contr...
Joshua E. Auerbach, Josh C. Bongard
SAB
2010
Springer
187views Optimization» more  SAB 2010»
15 years 7 days ago
Learning Robot-Environment Interaction Using Echo State Networks
Learning robot-environment interaction with echo state networks (ESNs) is presented in this paper. ESNs are asked to bootstrap a robot’s control policy from human teacher’s dem...
Mohamed Oubbati, Bahram Kord, Günther Palm