메뉴 건너뛰기




Volumn 64, Issue 2, 2004, Pages 279-293

Managing spatio-temporal complexity in Hopfield neural network simulations for large-scale static optimization

Author keywords

Artificial neural network; Computational complexity; Hopfield neural network; Huge data array; Large scale simulation; Optimization; Simulation; Weight matrix

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER SIMULATION; DATA STRUCTURES; OPTIMIZATION; PARALLEL PROCESSING SYSTEMS; TOPOLOGY; VECTORS;

EID: 0346969700     PISSN: 03784754     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.matcom.2003.09.023     Document Type: Article
Times cited : (13)

References (15)
  • 1
    • 0002381070 scopus 로고    scopus 로고
    • Neural networks for combinatorial optimization: A review of more than a decade of research
    • Smith K. Neural networks for combinatorial optimization: a review of more than a decade of research. INFORMS J. Comput. 11(1):1999;15-34.
    • (1999) INFORMS J. Comput. , vol.11 , Issue.1 , pp. 15-34
    • Smith, K.1
  • 2
    • 0032777933 scopus 로고    scopus 로고
    • Extended Hopfield models for combinatorial optimization
    • Gall A.L., Zissimopoulos V. Extended Hopfield models for combinatorial optimization. IEEE Trans. Neural Networks. 10(1):1999;72-80.
    • (1999) IEEE Trans. Neural Networks , vol.10 , Issue.1 , pp. 72-80
    • Gall, A.L.1    Zissimopoulos, V.2
  • 3
    • 0032207549 scopus 로고    scopus 로고
    • Optimal Hopfield network for combinatorial optimization with linear cost function
    • Matsuda S. Optimal Hopfield network for combinatorial optimization with linear cost function. IEEE Trans. Neural Networks. 9(6):1998;1319-1330.
    • (1998) IEEE Trans. Neural Networks , vol.9 , Issue.6 , pp. 1319-1330
    • Matsuda, S.1
  • 4
    • 0000245492 scopus 로고    scopus 로고
    • Improving convergence and solution quality of Hopfield-type neural networks with augmented lagrange multipliers
    • Li S.Z. Improving convergence and solution quality of Hopfield-type neural networks with augmented lagrange multipliers. IEEE Trans. Neural Networks. 7(6):1996;1507-1516.
    • (1996) IEEE Trans. Neural Networks , vol.7 , Issue.6 , pp. 1507-1516
    • Li, S.Z.1
  • 5
    • 0030244360 scopus 로고    scopus 로고
    • Neural networks for process scheduling in real-time communication systems
    • Cavalieri S., Mirabella O. Neural networks for process scheduling in real-time communication systems. IEEE Trans. Neural Networks. 7(5):1996;1272-1285.
    • (1996) IEEE Trans. Neural Networks , vol.7 , Issue.5 , pp. 1272-1285
    • Cavalieri, S.1    Mirabella, O.2
  • 6
    • 0031579671 scopus 로고    scopus 로고
    • On the performance of Hopfield network for graph search problem
    • Serpen G., Parvin A. On the performance of Hopfield network for graph search problem. Neurocomput.: Int. J. 14:1997;365-381.
    • (1997) Neurocomput.: Int. J. , vol.14 , pp. 365-381
    • Serpen, G.1    Parvin, A.2
  • 7
    • 0020118274 scopus 로고
    • Neural networks and physical systems with emergent collective computational properties
    • Hopfield J.J. Neural networks and physical systems with emergent collective computational properties. Proc. Natl. Acad. Sci. U.S.A. 79:1982;2554-2558.
    • (1982) Proc. Natl. Acad. Sci. U.S.A. , vol.79 , pp. 2554-2558
    • Hopfield, J.J.1
  • 8
    • 0004469897 scopus 로고
    • Neurons with graded response have collective computational properties like those of two-state neurons
    • Hopfield J.J. Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. U.S.A. 81:1984;3088-3092.
    • (1984) Proc. Natl. Acad. Sci. U.S.A. , vol.81 , pp. 3088-3092
    • Hopfield, J.J.1
  • 9
    • 0021835689 scopus 로고
    • Neural computations of decisions in optimization problems
    • Hopfield J.J., Tank D.W. Neural computations of decisions in optimization problems. Biol. Cybern. 52:1985;141-152.
    • (1985) Biol. Cybern. , vol.52 , pp. 141-152
    • Hopfield, J.J.1    Tank, D.W.2
  • 10
    • 0022504321 scopus 로고
    • Computing with neural networks: A model
    • Hopfield J.J., Tank D.W. Computing with neural networks: a model. Science. 233:1986;625-632.
    • (1986) Science , vol.233 , pp. 625-632
    • Hopfield, J.J.1    Tank, D.W.2
  • 11
    • 0347054191 scopus 로고
    • Hardware supervised learning for cellular and hopfield neural networks
    • San Diego, CA, 4-9 June
    • M. Balsi, Hardware supervised learning for cellular and hopfield neural networks, in: Proceedings of World Congress on Neural Networks, San Diego, CA, 4-9 June, 1994 pp. 451-456.
    • (1994) Proceedings of World Congress on Neural Networks , pp. 451-456
    • Balsi, M.1
  • 12
    • 0035249952 scopus 로고    scopus 로고
    • An improved branching rule for the symmetric Traveling Salesman Problem
    • Shutler P.M.E. An improved branching rule for the symmetric Traveling Salesman Problem. J. Oper. Res. Soc. 52(2):2001;169-175.
    • (2001) J. Oper. Res. Soc. , vol.52 , Issue.2 , pp. 169-175
    • Shutler, P.M.E.1
  • 13
    • 84955621501 scopus 로고    scopus 로고
    • Exploring self-adaptive methods to improve the efficiency of generating approximate solutions to Traveling Salesman Problems using evolutionary programming
    • Springer-Verlag, Berlin
    • K. Chellapilla, D.B. Fogel, Exploring self-adaptive methods to improve the efficiency of generating approximate solutions to Traveling Salesman Problems using evolutionary programming, Evolutionary Programming, vol. VI, Springer-Verlag, Berlin, 1997 pp. 361-371.
    • (1997) Evolutionary Programming , vol.6 , pp. 361-371
    • Chellapilla, K.1    Fogel, D.B.2
  • 15
    • 0034284295 scopus 로고    scopus 로고
    • Determination of weights for relaxation recurrent neural networks
    • Serpen G., Livingston D.L. Determination of weights for relaxation recurrent neural networks. Neurocomput.: Int. J. 34(1-4):2000;145-168.
    • (2000) Neurocomput.: Int. J. , vol.34 , Issue.1-4 , pp. 145-168
    • Serpen, G.1    Livingston, D.L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.