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IJON
2002
154views more  IJON 2002»
15 years 5 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
CMOT
1999
143views more  CMOT 1999»
15 years 5 months ago
Structural Learning: Attraction and Conformity in Task-Oriented Groups
This study extends previous research that showed how informal social sanctions can backfire when members prefer friendship over enforcement of group norms. We use a type of neural...
James A. Kitts, Michael W. Macy, Andreas Flache
202
Voted
CISIS
2010
IEEE
15 years 4 months ago
Modeling of Stress-induced Regulatory Cascades Involving Transcription Factor Dimers
Regulatory cascades consisting of stress-induced gene modules and their transcriptional regulators were recently identified and quantitatively modeled using Artificial Neural Netwo...
Maria Manioudaki, Panayiota Poirazi
IJCNN
2006
IEEE
16 years 7 days ago
Optimal In-Place Learning and the Lobe Component Analysis
— It is difficult to map many existing learning algorithms onto biological networks because the former require a separate learning network. The computational basis of biological...
Juyang Weng, Nan Zhang 0002
170
Voted
GECCO
2009
Springer
15 years 10 months ago
The sensitivity of HyperNEAT to different geometric representations of a problem
HyperNEAT, a generative encoding for evolving artificial neural networks (ANNs), has the unique and powerful ability to exploit the geometry of a problem (e.g., symmetries) by enc...
Jeff Clune, Charles Ofria, Robert T. Pennock