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Volumn 24, Issue 1, 1996, Pages 15-24

Design and learning with cellular neural networks

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[No Author keywords available]

Indexed keywords


EID: 0002435788     PISSN: 00989886     EISSN: None     Source Type: Journal    
DOI: 10.1002/(SICI)1097-007X(199601/02)24:1<15::AID-CTA900>3.0.CO;2-5     Document Type: Article
Times cited : (29)

References (29)
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    • Chua, L.O.1    Thiran, P.2
  • 3
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    • Membrain: A cellular neural network model based on a vibrating membrane
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  • 4
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    • On the applications of the cellular neural network paradigm in mechanical vibrating systems
    • P. Szolgay, G. Voros and G. Eross, 'On the applications of the cellular neural network paradigm in mechanical vibrating systems', IEEE Trans. Circuits and Systems O, CAS-40, 222-227 (1993).
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    • Szolgay, P.1    Voros, G.2    Eross, G.3
  • 7
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    • A learning algorithm for discrete-time cellular neural networks
    • Singapore, November
    • H.Harrer, J.A. Nossek and F.Zou, 'A learning algorithm for discrete-time cellular neural networks', IJCNN'91 Proc., Singapore, November 1991, pp.717-722.
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    • Harrer, H.1    Nossek, J.A.2    Zou, F.3
  • 9
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    • The learning problem for discrete-time cellular neural networks as a combinatorial optimization problem
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    • Magnussen, H.1    Nossek, J.A.2    Chua, L.O.3
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    • T.Roska and L.O. Chua, 'The CNN universal machine: an analogic array computer', IEEE Trans. Circuits and Systems II, CAS-40, 163-173 (1993).
    • (1993) IEEE Trans. Circuits and Systems II , vol.CAS-40 , pp. 163-173
    • Roska, T.1    Chua, L.O.2
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    • The relaxation method for linear inequalities
    • S.Agmon, 'The relaxation method for linear inequalities', Can. J. Math., 6, 382-392, (1954).
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    • The AdaTron: An adaptive perception algorithm
    • J.K. Anlauf and M.Biehl, 'The AdaTron: an adaptive perception algorithm', Europhys. Lett., 10, 687-692 (1989).
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  • 22
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    • Learning state space trajectories in recurrent neural networks
    • B.A. Pearlmutter, 'Learning state space trajectories in recurrent neural networks'. Neural Comput., 1, 263-269 (1989).
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    • Efficient gradient computation for continuous and discrete time-dependent neural networks
    • Singapore, June
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.