메뉴 건너뛰기




Volumn 13, Issue 3, 2003, Pages 223-234

Simple recurrent network trained by RTRL and extended Kalman filter algorithms

Author keywords

Extended Kalman filter; Next symbol prediction; Recurrent neural networks; Recursive languages

Indexed keywords

BACKPROPAGATION; COGNITIVE SYSTEMS; COMPUTATIONAL METHODS; GRADIENT METHODS; KALMAN FILTERING; LEARNING ALGORITHMS; NATURAL LANGUAGE PROCESSING SYSTEMS; REAL TIME SYSTEMS; RECURSIVE FUNCTIONS;

EID: 0037624007     PISSN: 12100552     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (23)

References (19)
  • 1
    • 23044528237 scopus 로고    scopus 로고
    • Finite-state Reber automaton and the recurrent neural networks trained in supervised and unsupervised manner
    • In: H. Bischof, G. Dorffner and K. Hornik (eds); Springer-Verlag
    • Čerňanský M., Beňušková L.: Finite-state Reber automaton and the recurrent neural networks trained in supervised and unsupervised manner. In: H. Bischof, G. Dorffner and K. Hornik (eds), LNCS 2130. Artificial Neural Networks - ICANN'2001, Springer-Verlag, 2001, pp. 737-742.
    • (2001) LNCS 2130. Artificial Neural Networks - ICANN'2001 , pp. 737-742
    • Čerňanský, M.1    Beňušková, L.2
  • 2
    • 0033212321 scopus 로고    scopus 로고
    • Toward a connectionist model of recursion in human linguistic performance
    • Christiansen, M. H., Chater, N.: Toward a connectionist model of recursion in human linguistic performance. Cognitive Sci., 23, 1999, pp. 417-437.
    • (1999) Cognitive Sci. , vol.23 , pp. 417-437
    • Christiansen, M.H.1    Chater, N.2
  • 3
    • 26444565569 scopus 로고
    • Finding structure in time
    • Elman J. L.: Finding structure in time. Cognitive Sci., 14, 1990, pp. 179-211.
    • (1990) Cognitive Sci. , vol.14 , pp. 179-211
    • Elman, J.L.1
  • 4
    • 0042326343 scopus 로고    scopus 로고
    • Recurrent neural networks with small weights implement finite memory machines
    • Hammer B., Tiño P.: Recurrent neural networks with small weights implement finite memory machines. To appear in Neural Computation.
    • Neural Computation
    • Hammer, B.1    Tiño, P.2
  • 5
    • 0038087303 scopus 로고
    • The origin of clusters in recurrent neural network state space
    • Lawrence Erlbaum Associates
    • Kolen, J. F.: The origin of clusters in recurrent neural network state space. In: Proc. 16th Annual Conf. of the Cognitive Sci. Soc., Hillsdale, NJ: Lawrence Erlbaum Associates, 1994, pp. 508-513.
    • (1994) Proc. 16th Annual Conf. of the Cognitive Sci. Soc., Hillsdale, NJ , pp. 508-513
    • Kolen, J.F.1
  • 6
    • 33747598711 scopus 로고    scopus 로고
    • Natural language grammatical inference with recurrent neural networks
    • Lawrence S., Giles C. L., Fong S.: Natural language grammatical inference with recurrent neural networks. IEEE Trans. Knowledge and Data Engineering, 12, 1, 2000, pp. 126-140.
    • (2000) IEEE Trans. Knowledge and Data Engineering , vol.12 , Issue.1 , pp. 126-140
    • Lawrence, S.1    Giles, C.L.2    Fong, S.3
  • 8
    • 0038426003 scopus 로고    scopus 로고
    • Modeling nonlinear dynamics with extended Kalman filter trained recurrent multilayer perceptrons
    • Thesis, McMaster Univ., Canada
    • Patel G. S.: Modeling nonlinear dynamics with extended Kalman filter trained recurrent multilayer perceptrons. Thesis, McMaster Univ., Canada, 2000.
    • (2000)
    • Patel, G.S.1
  • 9
    • 0038764011 scopus 로고    scopus 로고
    • Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets
    • Pérez-Ortiz J. A., Gers F. A., Eck D., Schmidhuber J.: Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets. Neural Networks, 16, 2, 2003, pp. 1-23.
    • (2003) Neural Networks , vol.16 , Issue.2 , pp. 1-23
    • Pérez-Ortiz, J.A.1    Gers, F.A.2    Eck, D.3    Schmidhuber, J.4
  • 10
    • 0035462333 scopus 로고    scopus 로고
    • Simple recurrent networks learn contex-free and contex-sensitive languages by counting
    • Rodriguez P.: Simple recurrent networks learn contex-free and contex-sensitive languages by counting. Neural Computation, 13, 2001, pp. 2093-2118.
    • (2001) Neural Computation , vol.13 , pp. 2093-2118
    • Rodriguez, P.1
  • 12
    • 0042827445 scopus 로고    scopus 로고
    • Architectural bias in recurrent neural networks - Fractal analysis
    • Tiño P., Hammer B.: Architectural bias in recurrent neural networks - fractal analysis. To appear in Neural Computation.
    • Neural Computation
    • Tiño, P.1    Hammer, B.2
  • 14
    • 79955750805 scopus 로고
    • An introduction to the Kalman filter
    • TR95-041, Dept. Computer Science, Univ. North Carolina
    • Welch G., Bishop G.: An introduction to the Kalman filter. TR95-041, Dept. Computer Science, Univ. North Carolina, 1995.
    • (1995)
    • Welch, G.1    Bishop, G.2
  • 15
    • 0025503558 scopus 로고
    • Backpropagation through time; what it does and how to do it
    • Werbos P. J.: Backpropagation through time; what it does and how to do it. Proceedings of the IEEE, 78, 1990, pp. 1550-1560.
    • (1990) Proceedings of the IEEE , vol.78 , pp. 1550-1560
    • Werbos, P.J.1
  • 16
    • 0013320371 scopus 로고
    • Some observations on the use of the extended Kalman filter as a recurrent network learning algorithm
    • TR NU-CCS-92-1, Boston
    • Williams R. J.: Some observations on the use of the extended Kalman filter as a recurrent network learning algorithm. TR NU-CCS-92-1, Boston, 1992.
    • (1992)
    • Williams, R.J.1
  • 17
    • 85132302281 scopus 로고
    • Training recurrent networks using the extended Kalman filter
    • Baltimore, June
    • Williams R. J.: Training recurrent networks using the extended Kalman filter. In: Proc. Intl. Joint Conf. Neural Networks, 4, Baltimore, June 1992, pp. 241-246.
    • (1992) Proc. Intl. Joint Conf. Neural Networks , vol.4 , pp. 241-246
    • Williams, R.J.1
  • 18
    • 0001202594 scopus 로고
    • A learning algorithm for continually running fully recurrent neural networks
    • Williams R. J., Zipser D.: A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1, 1989 pp. 270-280.
    • (1989) Neural Computation , vol.1 , pp. 270-280
    • Williams, R.J.1    Zipser, D.2
  • 19
    • 0001765578 scopus 로고
    • Gradient-based learning algorithms for recurrent networks and their computational complexity
    • In: Y. Chauvin and D. E. Rumelhãrt (eds); Lawrence Erlbaum Publishers, Hillsdale, N. J.
    • Williams R. J., Zipser D.: Gradient-based learning algorithms for recurrent networks and their computational complexity. In: Y. Chauvin and D. E. Rumelhãrt (eds). Backpropagation: Theory, Architectures and Applications, Lawrence Erlbaum Publishers, Hillsdale, N. J., 1995, pp. 433-486.
    • (1995) Backpropagation: Theory, Architectures and Applications , pp. 433-486
    • Williams, R.J.1    Zipser, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.