메뉴 건너뛰기




Volumn 5, Issue 11, 2009, Pages 849-856

A modified conjugate gradient formula for back propagation neural network algorithm

Author keywords

Back propagation algorithm; Conjugate gradient algorithm; Neural network algorithm; Search directions

Indexed keywords


EID: 77952261780     PISSN: 15493636     EISSN: None     Source Type: Journal    
DOI: 10.3844/jcssp.2009.849.856     Document Type: Article
Times cited : (14)

References (16)
  • 1
    • 0000646059 scopus 로고
    • Learning Internal Representations by Error Propagation
    • Rumelhart, D.E. and J.L. McClelland (Eds.). MIT Press, ISBN: 0-262-68053-X
    • Rumelhart, D.E., G.E. Hinton and R.J. Williams, 1986. Learning Internal Representations by Error Propagation. In: Parallel Distributed Processing, Rumelhart, D.E. and J.L. McClelland (Eds.). MIT Press, ISBN: 0-262-68053-X, pp: 318-362.
    • (1986) Parallel Distributed Processing , pp. 318-362
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 2
    • 0026745182 scopus 로고
    • On the problem of local minima in back-propagation
    • Gori, M. and A. Tesi, 1992. On the problem of local minima in back-propagation. IEEE Trans. Patt. Anal. Mach. Intel., 14: 76-86. http://doi.ieeecomputersociety.org/10.1109/34.107014
    • (1992) IEEE Trans. Patt. Anal. Mach. Intel. , vol.14 , pp. 76-86
    • Gori, M.1    Tesi, A.2
  • 4
    • 0000387235 scopus 로고
    • A rapidly convergent descent method for minimization
    • Fletcher, R. and M.J.D. Powell, 1963. A rapidly convergent descent method for minimization. Comput. J., 6: 163-168. http://comjnl.oxfordjournals.org/cgi/content/abstract/6/2/163
    • (1963) Comput. J. , vol.6 , pp. 163-168
    • Fletcher, R.1    Powell, M.J.D.2
  • 5
    • 0000615669 scopus 로고
    • Function minimization by conjugate gradients
    • DOI: 10.1093/comjnl/7.2.149
    • Fletcher, R. and C.M. Reeves, 1964. Function minimization by conjugate gradients. Comput. J., 7: 149-160. DOI: 10.1093/comjnl/7.2.149
    • (1964) Comput. J. , vol.7 , pp. 149-160
    • Fletcher, R.1    Reeves, C.M.2
  • 6
    • 0000135303 scopus 로고
    • Methods of conjugate gradients for solving linear systems
    • Hestenes, M.R. and E. Stiefel, 1952. Methods of conjugate gradients for solving linear systems. J. Res. Natl. Bureau Stand., 49: 409-435. http://nvl.nist.gov/pub/nistpubs/jres/049/6/V49.N06.A08.pdf
    • (1952) J. Res. Natl. Bureau Stand. , vol.49 , pp. 409-435
    • Hestenes, M.R.1    Stiefel, E.2
  • 7
    • 0014794877 scopus 로고
    • A unified approach to quadratically convergent algorithms for function minimization
    • DOI: 10.1007/BF00927440
    • Huang, H.Y., 1970. A unified approach to quadratically convergent algorithms for function minimization. J. Optim. Theor. Appli., 5: 405-423. DOI: 10.1007/BF00927440
    • (1970) J. Optim. Theor. Appli. , vol.5 , pp. 405-423
    • Huang, H.Y.1
  • 8
    • 0030584163 scopus 로고    scopus 로고
    • The Interchangeability of learning rate and gain in back propagation neural networks
    • DOI: 10.1162/neco.1996.8.2.451
    • Thimm, G., P. Moerland and E. Fiesler, 1996. The Interchangeability of learning rate and gain in back propagation neural networks. Neural Comput., 8: 451-460. DOI: 10.1162/neco.1996.8.2.451
    • (1996) Neural Comput , vol.8 , pp. 451-460
    • Thimm, G.1    Moerland, P.2    Fiesler, E.3
  • 9
    • 0032051569 scopus 로고    scopus 로고
    • The effect of internal parameters and geometry on the performance of back-propagation neural networks
    • DOI: 10.1016/S1364-8152(98)00020-6
    • Maier, H.R. and G.C. Dandy, 1998. The effect of internal parameters and geometry on the performance of back-propagation neural networks. Environ. Model. Software, 13: 193-209. DOI: 10.1016/S1364-8152(98)00020-6
    • (1998) Environ. Model. Software , vol.13 , pp. 193-209
    • Maier, H.R.1    Dandy, G.C.2
  • 10
    • 0037239622 scopus 로고    scopus 로고
    • Performance Improvement of Back propagation algorithm by automatic activation function gain tuning using fuzzy logic
    • DOI: 10.1016/S0925-2312(02)00576-3
    • Eom, K., K. Jung and H. Sirisena, 2003. Performance Improvement of Back propagation algorithm by automatic activation function gain tuning using fuzzy logic. Neurocomputing, 50: 439-460. DOI: 10.1016/S0925-2312(02)00576-3
    • (2003) Neurocomputing , vol.50 , pp. 439-460
    • Eom, K.1    Jung, K.2    Sirisena, H.3
  • 12
    • 85026821323 scopus 로고    scopus 로고
    • An improved learning algorithm based on conjugate gradient methods for back propagation neural network
    • Nawi, N.M., M.R. Ransing and R.S. Ransing, 2006. An improved learning algorithm based on conjugate gradient methods for back propagation neural network. Proc. Word Acad. Sci. Eng. Technol., 4: 46-54. http://www.waset.org/ijci/v4/v4-1-6.pdf
    • (2006) Proc. Word Acad. Sci. Eng. Technol. , vol.4 , pp. 46-54
    • Nawi, N.M.1    Ransing, M.R.2    Ransing, R.S.3
  • 13
    • 0000764772 scopus 로고
    • The use of multiple measurements in taxonomic problems
    • Fisher, R.A., 1936. The use of multiple measurements in taxonomic problems. Ann. Eugen., 7: 179-188. http://www.mendeley.com/c/85931226/Fisher-1936-The-use-of-multiple-measurements-intaxonomic-problems/
    • (1936) Ann. Eugen. , vol.7 , pp. 179-188
    • Fisher, R.A.1
  • 14
    • 77952277938 scopus 로고
    • A new non-quadratic model for unconstrained nonlinear optimization
    • Al Bayati, A., 1993. A new non-quadratic model for unconstrained nonlinear optimization. Mutah. J. Res. Stud., 8: 131-155.
    • (1993) Mutah. J. Res. Stud. , vol.8 , pp. 131-155
    • Al Bayati, A.1
  • 15
    • 0021457932 scopus 로고
    • A variablemetric method using a nonquadratic model
    • DOI: 10.1007/BF00934462
    • Tassopoulus, A. and C. Story, 1984. A variablemetric method using a nonquadratic model. J. Optim. Theor. Appli., 43: 383-393. DOI: 10.1007/BF00934462
    • (1984) J. Optim. Theor. Appli. , vol.43 , pp. 383-393
    • Tassopoulus, A.1    Story, C.2
  • 16
    • 0018431929 scopus 로고
    • A conjugate gradient optimization method invariant to non-linear scaling
    • Boland, W.R., E.R. Kamgnia and J.S. Kowallik, 1979. A conjugate gradient optimization method invariant to non-linear scaling. J. Optim. Theor. Appli., 27: 221-230. http://www.springerlink.com/index/T514NN45788701V4.pdf
    • (1979) J. Optim. Theor. Appli. , vol.27 , pp. 221-230
    • Boland, W.R.1    Kamgnia, E.R.2    Kowallik, J.S.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.