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» Using model knowledge for learning inverse dynamics
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ESANN
1998
13 years 8 months ago
Lazy learning for control design
This paper presents two local methods for the control of discrete-time unknown nonlinear dynamical systems, when only a limited amount of input-output data is available. The modeli...
Gianluca Bontempi, Mauro Birattari, Hugues Bersini
RC
1998
41views more  RC 1998»
13 years 7 months ago
Estimating Uncertainties for Geophysical Tomography
We present statistical and interval techniques for evaluating the uncertainties associated with geophysical tomographic inversion problems, including estimation of data errors, mo...
Diane I. Doser, Kevin D. Crain, Mark R. Baker, Vla...
NIPS
2003
13 years 8 months ago
Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System
We present a connectionist architecture that can learn a model of the relations between perceptions and actions and use this model for behavior planning. State representations are...
Marc Toussaint
COLT
2003
Springer
14 years 20 days ago
Learning Random Log-Depth Decision Trees under the Uniform Distribution
We consider three natural models of random logarithmic depth decision trees over Boolean variables. We give an efficient algorithm that for each of these models learns all but an ...
Jeffrey C. Jackson, Rocco A. Servedio
TNN
2008
171views more  TNN 2008»
13 years 7 months ago
Adaptive Dynamic Inversion via Time-Scale Separation
Abstract--This paper presents a full state feedback adaptive dynamic inversion method for uncertain systems that depend nonlinearly upon the control input. Using a specialized set ...
Naira Hovakimyan, E. Lavretsky, Chengyu Cao