메뉴 건너뛰기




Volumn 2, Issue 3, 2007, Pages 450-458

A novel algorithm for designing three layered artificial neural networks

Author keywords

ANN; Back propagation; Correlations; Ensemble of ANNs; Generalization; Overfitting

Indexed keywords


EID: 51549113917     PISSN: 18169503     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (4)

References (27)
  • 1
    • 84945797434 scopus 로고
    • ADynamic neuron creation in back-propagation networks
    • Ash, T., 1989. ADynamic neuron creation in back-propagation networks. Connection Sci., 1: 365-375.
    • (1989) Connection Sci. , vol.1 , pp. 365-375
    • Ash, T.1
  • 6
    • 28444491395 scopus 로고    scopus 로고
    • A novel information geometric approach to variable selection in MLP networks
    • Eleuteri, A., R. Tagliaferri andL. Milano, 2005. A novel information geometric approach to variable selection in MLP networks. Neural Networks, 18: 1309-1318.
    • (2005) Neural Networks , vol.18 , pp. 1309-1318
    • Eleuteri, A.1    Tagliaferri, R.2    Milano, L.3
  • 7
    • 0035505658 scopus 로고    scopus 로고
    • A New Pruning Heuristic Based on Variance Analysis of Sensitivity Information
    • Engelbrecht, A.P., 2001. A New Pruning Heuristic Based on Variance Analysis of Sensitivity Information. IEEE Trans. Neural Networks, 12: 1386-399.
    • (2001) IEEE Trans. Neural Networks , vol.12 , pp. 1386-399
    • Engelbrecht, A.P.1
  • 10
    • 0028413960 scopus 로고
    • Simple and effective method for removal of hidden units and weights
    • Hagiwara, M., 1994. Simple and effective method for removal of hidden units and weights. Neurocomputing, 6: 207-218.
    • (1994) Neurocomputing , vol.6 , pp. 207-218
    • Hagiwara, M.1
  • 13
    • 13844256702 scopus 로고    scopus 로고
    • Generalized Growing and Pruning RBF (CGAP-RBF) Neural Network for Function Approximation
    • Huang, G.B., P. Saratchandran andN. Sundararajan, 2005. Generalized Growing and Pruning RBF (CGAP-RBF) Neural Network for Function Approximation. IEEE Trans. Neural Networks, 16: 57-67.
    • (2005) IEEE Trans. Neural Networks , vol.16 , pp. 57-67
    • Huang, G.B.1    Saratchandran, P.2    Sundararajan, N.3
  • 14
    • 0042525838 scopus 로고    scopus 로고
    • Constructive Algorithm for Training Cooperative Neural Network Ensemble
    • Islam, M.M., X. Yao and K. Murase, 2003. Constructive Algorithm for Training Cooperative Neural Network Ensemble. IEEETrans. Neural Networks, 14: 820-832.
    • (2003) IEEETrans. Neural Networks , vol.14 , pp. 820-832
    • Islam, M.M.1    Yao, X.2    Murase, K.3
  • 15
    • 0031146959 scopus 로고    scopus 로고
    • Constructive algorithms for structure learning in feedforward neural networks for regression problems
    • Kwok, T.Y. and D.Y. Yeung, 1999. Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Trans. Neural Networks, 8: 630-645.
    • (1999) IEEE Trans. Neural Networks , vol.8 , pp. 630-645
    • Kwok, T.Y.1    Yeung, D.Y.2
  • 19
    • 0027149401 scopus 로고
    • Evolutional development of a multilevel neural network
    • Odri, S.V., D.P. Petrovacki and G.A. Krstonosic, 1993. Evolutional development of a multilevel neural network. Neural Networks, 6: 583-595.
    • (1993) Neural Networks , vol.6 , pp. 583-595
    • Odri, S.V.1    Petrovacki, D.P.2    Krstonosic, G.A.3
  • 20
    • 0029412003 scopus 로고
    • Some notes on neural learning algorithm benchmarking
    • Prechelt, L., 1995. Some notes on neural learning algorithm benchmarking. Neurocomputing, 9: 343-347.
    • (1995) Neurocomputing , vol.9 , pp. 343-347
    • Prechelt, L.1
  • 21
    • 0030130727 scopus 로고    scopus 로고
    • A quantitative study of experimental evaluation of neural network learning algorithms
    • Prechelt, L., 1996. A quantitative study of experimental evaluation of neural network learning algorithms. Neural Network, 9: 457-462.
    • (1996) Neural Network , vol.9 , pp. 457-462
    • Prechelt, L.1
  • 25
    • 11244307933 scopus 로고    scopus 로고
    • Geometric Interpretation and Architecture Selection of MLP
    • Xiang, C, Q. Ding and T. H. Lee, 2005. Geometric Interpretation and Architecture Selection of MLP. IEEE Trans. Neural Networks, 16: 84-96.
    • (2005) IEEE Trans. Neural Networks , vol.16 , pp. 84-96
    • Xiang, C.1    Ding, Q.2    Lee, T.H.3
  • 26
    • 0031143030 scopus 로고    scopus 로고
    • A new evolutionary system for evolving artificial neural networks
    • Yao, X. and Y. Liu, 1997. A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Networks, 8: 694-713.
    • (1997) IEEE Trans. Neural Networks , vol.8 , pp. 694-713
    • Yao, X.1    Liu, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.