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Volumn 11, Issue 5, 2000, Pages 1041-1049

Neural-network methods for boundary value problems with irregular boundaries

Author keywords

[No Author keywords available]

Indexed keywords

PENALTY METHOD; RADIAL BASIS FUNCTION (RBF) NETWORKS;

EID: 0034270127     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.870037     Document Type: Article
Times cited : (505)

References (12)
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    • (1998) IEEE Trans. Neural Networks , vol.9 , pp. 987-1000
    • Lagaris, I.E.1    Likas, A.2    Fotiadis, D.I.3
  • 2
    • 0031207136 scopus 로고    scopus 로고
    • Artificial neural network methods in quantum mechanics
    • _, "Artificial neural network methods in quantum mechanics," Comput. Phys. Commun., vol. 104, pp. 1-14, 1997.
    • (1997) Comput. Phys. Commun. , vol.104 , pp. 1-14
  • 4
    • 0025401662 scopus 로고
    • Flow and heat transfer in CVD reactors: Comparison of Raman temperature measurement and finite element method prediction
    • D. I. Fotiadis, M. Boekholt, K. Jensen, and W. Richter, "Flow and heat transfer in CVD reactors: Comparison of Raman temperature measurement and finite element method prediction," J. Crystal Growth, vol. 100, pp. 577-599, 1990.
    • (1990) J. Crystal Growth , vol.100 , pp. 577-599
    • Fotiadis, D.I.1    Boekholt, M.2    Jensen, K.3    Richter, W.4
  • 6
    • 0032045459 scopus 로고    scopus 로고
    • The Merlin control language for strategic optimization
    • _, "The Merlin control language for strategic optimization," Comput. Phys. Commun., vol. 109, pp. 250-275, 1998.
    • (1998) Comput. Phys. Commun. , vol.109 , pp. 250-275
  • 7
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Networks, vol. 2, pp. 359-366, 1989.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 8
    • 0027262895 scopus 로고
    • Multilayer feedforward networks with nonpolynomial activation function can approximate any function
    • M. Leshno, V. Lin, A. Pinkus, and S. Schocken, "Multilayer feedforward networks with nonpolynomial activation function can approximate any function," Neural Networks, vol. 6, pp. 861-867, 1993.
    • (1993) Neural Networks , vol.6 , pp. 861-867
    • Leshno, M.1    Lin, V.2    Pinkus, A.3    Schocken, S.4
  • 10
    • 0031653036 scopus 로고    scopus 로고
    • Neural-network training and simulation using a multidimensional optimization system
    • A. Likas, D. A. Karras, and I. E. Lagaris, "Neural-network training and simulation using a multidimensional optimization system," Int. J. Comput. Math., vol. 67, pp. 33-46, 1998.
    • (1998) Int. J. Comput. Math. , vol.67 , pp. 33-46
    • Likas, A.1    Karras, D.A.2    Lagaris, I.E.3
  • 12
    • 0001448449 scopus 로고    scopus 로고
    • An iterative method for nonsymmetric systems with multiple right-hand sides
    • V. Simoncini and E. Gallopoulos, "An iterative method for nonsymmetric systems with multiple right-hand sides," SIAM J. Sci. Comput., vol. 16, pp. 917-933, 1998.
    • (1998) SIAM J. Sci. Comput. , vol.16 , pp. 917-933
    • Simoncini, V.1    Gallopoulos, E.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.