Limitations and Future Trends in Neural Computation

0404167 - UIVT-O 20030180 RIV NL eng M - Část monografie knihy
Šíma, Jiří
Energy-Based Computation with Symmetric Hopfield Nets.
Limitations and Future Trends in Neural Computation. Amsterdam: IOS Press, 2003 - (Ablameyko, S.; Gori, M.; Goras, L.; Piuri, V.), s. 45-70. NATO Science Series, 186. ISBN 1-58603-324-7
Grant CEP: GA AV ČR IAB2030007; GA ČR GA201/01/1192
Výzkumný záměr: AV0Z1030915
Klíčová slova: Hopfield network * energy function * computational power * analog state * continuous time
Kód oboru RIV: BA - Obecná matematika

Citováno: 4

--- SERPEN, G. Hopfield network as static optimizer: Learning the weights and eliminating the guesswork. NEURAL PROCESSING LETTERS. ISSN 1370-4621, FEB 2008, vol. 27, no. 1, p. 1-15. [WOS]
--- Serpen G. Adaptation in Weight Space through Gradient Descent for Hopfield Network as Static Optimizer: Is it Feasible?” to appear in proceedings for the Artificial Neural Networks in Engineering Conference to be held in St. Louis, MO, November 2007.
--- Gardasevic, V. - Muller, R. - Ryan, D. - Lundheim, L. - Oien, G. Lattice-Reduction Aided HNN for Vector Precoding. Proceedings of ISITA 2010. IEEE, 2010, p. 37-41.
--- Gardasevic, V. - Muller, R. - Oien, C. Hopfield Neural Networks for Vector Precoding. Proceedings of Int. Zurich Seminar on Communications (IZS), March 3-5, 2010, p. 66-69.

Trvalý link: http://hdl.handle.net/11104/0124434