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Volumn 1, Issue , 2007, Pages 601-605

Neural networks for approximation of real functions with the Gaussian functions

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; ERROR ANALYSIS; GAUSSIAN DISTRIBUTION; INTERPOLATION;

EID: 38049039416     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNC.2007.498     Document Type: Conference Paper
Times cited : (10)

References (11)
  • 1
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    • Feedforward neural networks with arbitrary bounded nonlinear activation functions
    • G.B. Huang, H.A. Babri, Feedforward neural networks with arbitrary bounded nonlinear activation functions. IEEE Trans. Neural Networks, 9-1:224-229, 1998.
    • (1998) IEEE Trans. Neural Networks , vol.9 -1 , pp. 224-229
    • Huang, G.B.1    Babri, H.A.2
  • 2
    • 0026190194 scopus 로고
    • A simple method to derive bounds on the size and to train multilayer neural networks
    • M.A. Sartori, P.J. Antsaklis, A simple method to derive bounds on the size and to train multilayer neural networks. IEEE Trans. Neural Networks, 2-4:467-471, 1991.
    • (1991) IEEE Trans. Neural Networks , vol.2-4 , pp. 467-471
    • Sartori, M.A.1    Antsaklis, P.J.2
  • 3
    • 0031100287 scopus 로고    scopus 로고
    • Capabilities of a four-layered feedforward neural network
    • S. Tamura, M. Tateishi, Capabilities of a four-layered feedforward neural network. IEEE Trans. Neural Networks, 8-2:251-255, 1997.
    • (1997) IEEE Trans. Neural Networks , vol.8 -2 , pp. 251-255
    • Tamura, S.1    Tateishi, M.2
  • 4
    • 0033747328 scopus 로고    scopus 로고
    • Single iteration training algorithm for multilayer feedforward neural networks
    • J. Barhen, R. Cogswell, V. Protopopescu, Single iteration training algorithm for multilayer feedforward neural networks. Neural Process. Lett., 11:113-129, 2000.
    • (2000) Neural Process. Lett , vol.11 , pp. 113-129
    • Barhen, J.1    Cogswell, R.2    Protopopescu, V.3
  • 5
    • 0036644587 scopus 로고    scopus 로고
    • Interpolation by ridge polynomials and its application in neural networks
    • X. Li, Interpolation by ridge polynomials and its application in neural networks. J. Comput. Appl. Math., 144:197-209, 2002.
    • (2002) J. Comput. Appl. Math , vol.144 , pp. 197-209
    • Li, X.1
  • 6
    • 0026904597 scopus 로고
    • Feedforward nets for interpolation and classification
    • E.D. Sontag, Feedforward nets for interpolation and classification. J. Comp. Syst. Sci., 45:20-48, 1992.
    • (1992) J. Comp. Syst. Sci , vol.45 , pp. 20-48
    • Sontag, E.D.1
  • 8
    • 27844503288 scopus 로고    scopus 로고
    • Limitations of the approximation capabilities of neural networks with one hidden layer
    • C.K. Chui, X. Li, H.N. Mhaskar, Limitations of the approximation capabilities of neural networks with one hidden layer. Adv. Comput. Math., 5:233-243, 1996.
    • (1996) Adv. Comput. Math , vol.5 , pp. 233-243
    • Chui, C.K.1    Li, X.2    Mhaskar, H.N.3
  • 9
    • 51249165422 scopus 로고
    • Degree of approximation by superpositions of a sigmoidal function
    • C. Debao, Degree of approximation by superpositions of a sigmoidal function. Approx. Theory & its Appl., 9:17-28, 1993.
    • (1993) Approx. Theory & its Appl , vol.9 , pp. 17-28
    • Debao, C.1
  • 10
    • 0000358945 scopus 로고
    • Approximation by superposition of sigmoidal and radial basis functions
    • H.N. Mhaskar, Ch.A. Michelli, Approximation by superposition of sigmoidal and radial basis functions. Adv. Appl. Math., 13:350-373, 1992.
    • (1992) Adv. Appl. Math , vol.13 , pp. 350-373
    • Mhaskar, H.N.1    Michelli, C.A.2
  • 11
    • 28244460747 scopus 로고    scopus 로고
    • Constructive approximate interpolation by neural networks
    • B. Lianas, F. J. Sainz, Constructive approximate interpolation by neural networks. J. Comput. Appl. Math., 188:283-308, 2006.
    • (2006) J. Comput. Appl. Math , vol.188 , pp. 283-308
    • Lianas, B.1    Sainz, F.J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.