Energy Complexity of Recurrent Neural Networks

0393985 - ÚI 2015 RIV US eng J - Článek v odborném periodiku
Šíma, Jiří
Energy Complexity of Recurrent Neural Networks.
Neural Computation. Roč. 26, č. 5 (2014), s. 953-973. ISSN 0899-7667. E-ISSN 1530-888X
Grant CEP: GA ČR GAP202/10/1333
Institucionální podpora: RVO:67985807
Klíčová slova: neural network * finite automaton * energy complexity * optimal size
Kód oboru RIV: IN - Informatika
Impakt faktor: 2.207, rok: 2014

Citováno: 4

--- HAN, H.G. - LI, Y. - GUO, Y.N. - QIAO, J.F. A soft computing method to predict sludge volume index based on a recurrent self-organizing neural network. APPLIED SOFT COMPUTING. ISSN 1568-4946, JAN 2016, vol. 38, p. 477-486. [WOS]
--- Rui, L. - Hong, Y. - Wang, X. Observability of Automata Networks: Fixed and Switching Cases. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. online 10 March 2017
--- JUBA, B. Computational Complexity and the Function-Structure-Environment Loop of the Brain. CLOSED LOOP NEUROSCIENCE. 2016, p. 131-144. [WOS]
--- LI, R. - HONG, Y.G. - WANG, X.Y. Observability of Automata Networks: Fixed and Switching Cases. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. ISSN 2162-237X, APR 2018, vol. 29, no. 4, p. 1388-1394. [WOS]

Trvalý link: http://hdl.handle.net/11104/0222343