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Volumn 3610, Issue PART I, 2005, Pages 157-166

New training method and optimal structure of backpropagation networks

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; KALMAN FILTERING; NEURAL NETWORKS; PROBLEM SOLVING;

EID: 26844563713     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11539087_18     Document Type: Conference Paper
Times cited : (6)

References (24)
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    • Phien, H.N.1    Siang, J.J.2
  • 6
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    • Neural network system for forecasting method selection
    • Chu, C.H., Widjaja, D.: Neural Network System for Forecasting Method Selection. Decision Support Systems 12 (1994) 13-24
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    • Chu, C.H.1    Widjaja, D.2
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    • Methods to speed up error back-propagation learning algorithm
    • Sarkar, D.: Methods to speed up Error Back-Propagation Learning Algorithm. ACM Computing Surveys 27(4) (1995) 519-542
    • (1995) ACM Computing Surveys , vol.27 , Issue.4 , pp. 519-542
    • Sarkar, D.1
  • 8
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    • Multilayer feedforward networks are universal approximators
    • Hornik, K., Stinchcombe, M., White, H.: Multilayer Feedforward Networks are Universal Approximators. Neural Networks 2 (1989) 359-366
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    • Hornik, K.1    Stinchcombe, M.2    White, H.3
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    • A fast new algorithm for training feedforward neural networks
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    • Scalero, R.S.1    Tepedelenlioglu, N.2
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    • Function minimization by conjugate gradients
    • Fletcher, R., Reeves, C.M.: Function Minimization by Conjugate Gradients. Computer J. 7 (1964) 149-154
    • (1964) Computer J. , vol.7 , pp. 149-154
    • Fletcher, R.1    Reeves, C.M.2
  • 14
    • 0000135303 scopus 로고
    • Methods of conjugate gradients for solving linear systems
    • Hestense, M.R., Stierel, E.: Methods of Conjugate Gradients for Solving Linear Systems. J. Res. Nat. Bur. Standards Sec. B 48 (1952) 409-436
    • (1952) J. Res. Nat. Bur. Standards Sec. B , vol.48 , pp. 409-436
    • Hestense, M.R.1    Stierel, E.2
  • 15
    • 0000857773 scopus 로고
    • Identification of nonlinear processes using reciprocal multiquadratic functions
    • Pottmann, M., Seborg, D.E.: Identification of Nonlinear Processes using Reciprocal Multiquadratic Functions. J. Proc. Cont. 2(4) (1992) 189-203
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    • Pottmann, M.1    Seborg, D.E.2
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    • A new look at the statistical model identification
    • Akaike, H.: A New Look at the Statistical Model Identification. IEEE Trans. on Automatic Control AC-19(6) (1974) 716-723
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    • Akaike, H.1
  • 18
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    • A Bayesian comparison of different classes of dynamic models using empirical data
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.