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Volumn , Issue , 2008, Pages 445-449

Sub-domain intelligent modeling based on neural networks

Author keywords

[No Author keywords available]

Indexed keywords

DO-MAINS; LEVEL MODELS; MODELING METHODOLOGIES; MODELING METHODS; MULTIVARIATE PROCESSES; PERSISTENT EXCITATIONS; STEP BY STEPS;

EID: 56349151820     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2008.4633830     Document Type: Conference Paper
Times cited : (4)

References (11)
  • 1
    • 0033098832 scopus 로고    scopus 로고
    • Recurrent neuro-fuzzy networks for nonlinear process modeling
    • J. Zhang and A.J. Morris, "Recurrent neuro-fuzzy networks for nonlinear process modeling", IEEE Trans on Neural Networks, vol.10, no. 2, pp.313-326, 1999.
    • (1999) IEEE Trans on Neural Networks , vol.10 , Issue.2 , pp. 313-326
    • Zhang, J.1    Morris, A.J.2
  • 2
    • 0041376987 scopus 로고    scopus 로고
    • A neurofuzzy network knowledge extraction and extended Gram-Schmidt algorithm for model subspace decomposition
    • H. Xia and C.J. Harris, "A neurofuzzy network knowledge extraction and extended Gram-Schmidt algorithm for model subspace decomposition", IEEE Trans on Fuzzy Systems, vol.11, no. 4, pp.528-541, 2003.
    • (2003) IEEE Trans on Fuzzy Systems , vol.11 , Issue.4 , pp. 528-541
    • Xia, H.1    Harris, C.J.2
  • 3
    • 0033722255 scopus 로고    scopus 로고
    • Neurofuzzy network model construction using Bezier-Bemstein polynomial functions
    • C.J Harris and H. Xia, "Neurofuzzy network model construction using Bezier-Bemstein polynomial functions", IEE Proceed Control Theory and Applications, vol.147, no.3, pp.337-343,2000.
    • (2000) IEE Proceed Control Theory and Applications , vol.147 , Issue.3 , pp. 337-343
    • Harris, C.J.1    Xia, H.2
  • 4
    • 0028369322 scopus 로고
    • Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems
    • C.T. Lin and C.S.G Lee, "Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems", IEEE Trans on Fuzzy Systems, vol.2, no.1, pp.46-63, 1994.
    • (1994) IEEE Trans on Fuzzy Systems , vol.2 , Issue.1 , pp. 46-63
    • Lin, C.T.1    Lee, C.S.G.2
  • 5
    • 0035245192 scopus 로고    scopus 로고
    • Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks
    • H. Xia and C.J. Harris, "Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks", IEEE Trans on Fuzzy Systems, vol. 9, no.l, pp.88-101, 2001.
    • (2001) IEEE Trans on Fuzzy Systems , vol.9 , Issue.L , pp. 88-101
    • Xia, H.1    Harris, C.J.2
  • 6
    • 33747888908 scopus 로고    scopus 로고
    • A local neuro-fuzzy network for high-dimensional models and optimization
    • S. Jakubek and N. Keuth, "A local neuro-fuzzy network for high-dimensional models and optimization", Engineering Applications of Artificial Intelligence, vol.19, no.6, pp.705-717, 2006.
    • (2006) Engineering Applications of Artificial Intelligence , vol.19 , Issue.6 , pp. 705-717
    • Jakubek, S.1    Keuth, N.2
  • 7
    • 0032626496 scopus 로고    scopus 로고
    • FEM-based neural-network approach to nonlinear modeling with application to longitudinal vehicle dynamics control
    • J. Kalkkuhl, K.J Hunt, and H. Fritz, "FEM-based neural-network approach to nonlinear modeling with application to longitudinal vehicle dynamics control", IEEE Trans on Neural Networks, vol.10, no.4, pp.885-897, 1999.
    • (1999) IEEE Trans on Neural Networks , vol.10 , Issue.4 , pp. 885-897
    • Kalkkuhl, J.1    Hunt, K.J.2    Fritz, H.3
  • 8
    • 0009630815 scopus 로고    scopus 로고
    • An Insight Into the Standard Back-propagation Neural Network Model for Regression Analysis
    • S.H. Wang, "An Insight Into the Standard Back-propagation Neural Network Model for Regression Analysis", Omega, vol.26, no. 1, pp.133-140, 1998.
    • (1998) Omega , vol.26 , Issue.1 , pp. 133-140
    • Wang, S.H.1
  • 9
    • 0032123629 scopus 로고    scopus 로고
    • A hybrid linear/nonlinear training algorithm for feedforward neural networks
    • S. McLoone, M.D. Brown, G. Irwin, and A. Lightbody, "A hybrid linear/nonlinear training algorithm for feedforward neural networks", IEEE Trans on Neural Networks, vol.9, no. 4, pp.669-684, 1998.
    • (1998) IEEE Trans on Neural Networks , vol.9 , Issue.4 , pp. 669-684
    • McLoone, S.1    Brown, M.D.2    Irwin, G.3    Lightbody, A.4
  • 11
    • 0033366230 scopus 로고    scopus 로고
    • Fuzzy neural networks with application to sales forecasting
    • R. J. Kuo and K. C. Xue, "Fuzzy neural networks with application to sales forecasting", Fuzzy Sets and Systems, vol. 108, no. 2, pp.123-143, 1999.
    • (1999) Fuzzy Sets and Systems , vol.108 , Issue.2 , pp. 123-143
    • Kuo, R.J.1    Xue, K.C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.