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» Learning Chaotic Attractors by Neural Networks
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IJON
2002
154views more  IJON 2002»
13 years 6 months ago
Nonlinear model predictive control of a cutting process
Nonlinear model predictive control (MPC) of a simulated chaotic cutting process is presented. The nonlinear MPC combines a neural-network model and a genetic-algorithm-based optim...
Primoz Potocnik, Igor Grabec
NC
1998
140views Neural Networks» more  NC 1998»
13 years 7 months ago
Recurrent Neural Networks with Iterated Function Systems Dynamics
We suggest a recurrent neural network (RNN) model with a recurrent part corresponding to iterative function systems (IFS) introduced by Barnsley 1] as a fractal image compression ...
Peter Tiño, Georg Dorffner
IJCNN
2006
IEEE
14 years 9 days ago
Reinforcement Learning for Parameterized Motor Primitives
Abstract— One of the major challenges in both action generation for robotics and in the understanding of human motor control is to learn the “building blocks of movement genera...
Jan Peters, Stefan Schaal
IJON
2007
85views more  IJON 2007»
13 years 6 months ago
Hierarchical dynamical models of motor function
Hierarchical models of motor function are described in which the motor system encodes a hierarchy of dynamical motor primitives. The models are based on continuous attractor neura...
Simon M. Stringer, Edmund T. Rolls
AR
2008
188views more  AR 2008»
13 years 6 months ago
Intentional Control for Planetary Rover SRR
Intentional behavior is a basic property of intelligence and it incorporates the cyclic operation of prediction, testing by action, sensing, perceiving, and assimilating the exper...
Robert Kozma, Terry Huntsberger, Hrand Aghazarian,...