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Volumn 19, Issue 6, 2005, Pages 913-950

Use of temporal neural networks for prognosis and dynamic monitoring: Comparative studies of three recurrent neural networks;Utilisation des réseaux de neurones temporels pour le pronostic et la surveillance dynamique: Etude comparative de trois réseaux de neurones récurrents

Author keywords

DGNN; Dynamic monitoring; Learning; Prognosis; R2BF; Recurrent neural network; RRBF; Temporal neural network

Indexed keywords

DGNN; DYNAMIC MONITORING; LEARNING; PROGNOSIS; R2BF; RECURRENT NEURAL NETWORK; RRBF; TEMPORAL NEURAL NETWORK;

EID: 33645863461     PISSN: 0992499X     EISSN: None     Source Type: Journal    
DOI: 10.3166/ria.19.913-950     Document Type: Article
Times cited : (3)

References (24)
  • 2
    • 0000163157 scopus 로고    scopus 로고
    • Time in self-organizing maps: An overview of models
    • Barreto G., Araújo A., «Time in self-organizing maps: An overview of models», International Journal of Computer Research, vol. 10, no 2, p. 139-179, 2001.
    • (2001) International Journal of Computer Research , vol.10 , Issue.2 , pp. 139-179
    • Barreto, G.1    Araújo, A.2
  • 6
    • 84857417015 scopus 로고
    • Boosting the performance of RBF networks with dynamic decay adjustment
    • G. Tesauro, D. S. Touretzky, T. K. Leen (eds), MIT Press, Cambridge, MA
    • Berthold M. R., Diamond J., «Boosting the Performance of RBF Networks with Dynamic Decay Adjustment», in, G. Tesauro, , D. S. Touretzky, , T. K. Leen (eds), Advances in Neural Information Processing Systems, vol. 7, MIT Press, Cambridge, MA, p. 521-528, 1995.
    • (1995) Advances in Neural Information Processing Systems , vol.7 , pp. 521-528
    • Berthold, M.R.1    Diamond, J.2
  • 11
    • 26444565569 scopus 로고
    • Finding structure in time
    • Elman J. L., «Finding Structure in Time», Cognitive Science, vol. 2, no 14, p. 179-211, 1990.
    • (1990) Cognitive Science , vol.2 , Issue.14 , pp. 179-211
    • Elman, J.L.1
  • 12
    • 33645890843 scopus 로고    scopus 로고
    • Evolutionary design of dynamic neural networks applied to system identification
    • Ferariu L., Marcu T., «Evolutionary design of dynamic neural networks applied to system identification», 15th Triennal World Congress, IFAC, 2002.
    • (2002) 15th Triennal World Congress, IFAC
    • Ferariu, L.1    Marcu, T.2
  • 13
    • 0030125824 scopus 로고    scopus 로고
    • Representation of finite state automata in re-current radial basis function networks
    • Frasconi P., Gori M., Maggini M., Soda G., «Representation of Finite State Automata in Re-current Radial Basis Function Networks», Machine Learning, vol. 23, no 1, p. 5-32, 1996.
    • (1996) Machine Learning , vol.23 , Issue.1 , pp. 5-32
    • Frasconi, P.1    Gori, M.2    Maggini, M.3    Soda, G.4
  • 15
    • 12844260180 scopus 로고
    • A temporal connectionist approach to natural language
    • Juillet
    • Jacquemin C., «A Temporal Connectionist Approach to Natural Language», SIGART Bulletin, vol. 5, no 3, p. 12-22, Juillet, 1994.
    • (1994) SIGART Bulletin , vol.5 , Issue.3 , pp. 12-22
    • Jacquemin, C.1
  • 16
    • 0004121079 scopus 로고
    • Serial order: A parallel distributed processing approach
    • Institute for Cognitive Science, University of California, San Diego
    • Jordan M. I., Serial Order: A Parallel Distributed Processing Approach, Technical Report no ICS Report No. 8604, Institute for Cognitive Science, University of California, San Diego, 1986.
    • (1986) Technical Report No ICS Report No. 8604 , vol.8604
    • Jordan, M.I.1
  • 17
    • 0039870976 scopus 로고
    • A time delay neural network architecture for speech recognition
    • Carnegie-Mellon University, Pittsburgh PA
    • Lang K. J., Hinton G. E., A time delay neural network architecture for speech recognition, Technical Report no CMU-cs-88-152, Carnegie-Mellon University, Pittsburgh PA, 1988.
    • (1988) Technical Report No CMU-cs-88-152 , vol.CMU-CS-88-152
    • Lang, K.J.1    Hinton, G.E.2
  • 18
    • 34250122797 scopus 로고
    • Interpolation of scattered data: Distance matrices and conditionally positive definite functions
    • Michelli C. A., «Interpolation of scattered data: distance matrices and conditionally positive definite functions», Constructive Approximation, vol. 2, p. 11-22, 1986.
    • (1986) Constructive Approximation , vol.2 , pp. 11-22
    • Michelli, C.A.1
  • 19
    • 0000672424 scopus 로고
    • Fast learning in networks of locally-tuned processing units
    • Moody J., Darken C., «Fast learning in networks of locally-tuned processing units», Neural Computation, vol. 1, no 2, p. 281-294, 1989.
    • (1989) Neural Computation , vol.1 , Issue.2 , pp. 281-294
    • Moody, J.1    Darken, C.2
  • 21
    • 0032108106 scopus 로고    scopus 로고
    • Evolutionary algorithms and gradient search: Similarities and differences
    • July
    • Salomon R., «Evolutionary Algorithms and Gradient Search: Similarities and Differences», IEEE Transactions on Evolutionary Computation, vol. 2, no 2, p. 45-55, July, 1998.
    • (1998) IEEE Transactions on Evolutionary Computation , vol.2 , Issue.2 , pp. 45-55
    • Salomon, R.1
  • 24
    • 0142146523 scopus 로고    scopus 로고
    • Réseaux de neurones récurrents à fonctions de base radiales: RRFR, application au pronostic
    • Zemouri R., Racoceanu D., Zerhouni N., «Réseaux de neurones récurrents à fonctions de base radiales: RRFR, application au pronostic», Revue d'Intelligence Artificielle, RSTI série RIA, vol. 16, no 3, p. 307-338, 2003.
    • (2003) Revue d'Intelligence Artificielle, RSTI Série RIA , vol.16 , Issue.3 , pp. 307-338
    • Zemouri, R.1    Racoceanu, D.2    Zerhouni, N.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.