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» Explanation-Based Neural Network Learning for Robot Control
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TSMC
2008
177views more  TSMC 2008»
14 years 10 months 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, ...
83
Voted
ISCAS
2005
IEEE
154views Hardware» more  ISCAS 2005»
15 years 5 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 5 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 3 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»
14 years 10 months 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