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Editorial.
- Published in:
- 2004
- By:
- Publication type:
- Editorial
Parametric identification of robotic systems with stable time-varying Hopfield networks.
- Published in:
- Neural Computing & Applications, 2004, v. 13, n. 4, p. 270, doi. 10.1007/s00521-004-0421-4
- By:
- Publication type:
- Article
RealNet: a neural network architecture for real-time systems scheduling.
- Published in:
- Neural Computing & Applications, 2004, v. 13, n. 4, p. 281, doi. 10.1007/s00521-004-0422-3
- By:
- Publication type:
- Article
Neural network-based analog fault diagnosis using testability analysis.
- Published in:
- Neural Computing & Applications, 2004, v. 13, n. 4, p. 288, doi. 10.1007/s00521-004-0423-2
- By:
- Publication type:
- Article
Neural networks for the EMOBOT robot control architecture.
- Published in:
- Neural Computing & Applications, 2004, v. 13, n. 4, p. 299, doi. 10.1007/s00521-004-0424-1
- By:
- Publication type:
- Article
An approach to the analysis of thickness deviations in stainless steel coils based on self-organising map neural networks.
- Published in:
- Neural Computing & Applications, 2004, v. 13, n. 4, p. 309, doi. 10.1007/s00521-004-0426-z
- By:
- Publication type:
- Article
Neural-network-based stable control by using harmonic analysis.
- Published in:
- Neural Computing & Applications, 2004, v. 13, n. 4, p. 316, doi. 10.1007/s00521-004-0425-0
- By:
- Publication type:
- Article
New supervision architecture based on on-line modelling of non-stationary data.
- Published in:
- Neural Computing & Applications, 2004, v. 13, n. 4, p. 323, doi. 10.1007/s00521-004-0427-y
- By:
- Publication type:
- Article
On three intelligent systems: dynamic neural, fuzzy, and wavelet networks for training trajectory.
- Published in:
- Neural Computing & Applications, 2004, v. 13, n. 4, p. 339, doi. 10.1007/s00521-004-0429-9
- By:
- Publication type:
- Article
Neural network modeling supports a theory on the hierarchical control of prehension.
- Published in:
- Neural Computing & Applications, 2004, v. 13, n. 4, p. 352, doi. 10.1007/s00521-004-0430-3
- By:
- Publication type:
- Article
Can we learn anything from single-channel unaveraged MEG data?
- Published in:
- Neural Computing & Applications, 2004, v. 13, n. 4, p. 360, doi. 10.1007/s00521-004-0432-1
- By:
- Publication type:
- Article