메뉴 건너뛰기




Volumn 27, Issue 2, 1997, Pages 208-215

Computational capabilities of recurrent NARX neural networks

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; AUTOMATA THEORY; COMPUTATIONAL METHODS; COMPUTER SIMULATION; FEEDBACK; FUNCTIONS; LEARNING SYSTEMS; MATHEMATICAL MODELS; NONLINEAR SYSTEMS; TURING MACHINES;

EID: 0031124173     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/3477.558801     Document Type: Article
Times cited : (382)

References (30)
  • 1
    • 51249194645 scopus 로고
    • A logical calculus of the ideas immanent in nervous activity
    • W. McCulloch and W. Pitts, "A logical calculus of the ideas immanent in nervous activity," Bull. Math. Biophys., vol. 5, pp. 115-133, 1943.
    • (1943) Bull. Math. Biophys. , vol.5 , pp. 115-133
    • McCulloch, W.1    Pitts, W.2
  • 3
    • 0011252246 scopus 로고
    • Efficient simulation of finite automata by neural nets
    • N. Alon, A. Dewdney, and T. Ott, "Efficient simulation of finite automata by neural nets," J. Assoc. Comput. Mach., vol. 38, no. 2, pp. 495-514, 1991.
    • (1991) J. Assoc. Comput. Mach. , vol.38 , Issue.2 , pp. 495-514
    • Alon, N.1    Dewdney, A.2    Ott, T.3
  • 4
    • 0030110968 scopus 로고    scopus 로고
    • Bounds on the complexity of recurrent neural network implementations of finite state machines
    • B. Horne and D. Hush, "Bounds on the complexity of recurrent neural network implementations of finite state machines," Neural Networks, vol. 9, no. 2, pp. 243-252, 1996.
    • (1996) Neural Networks , vol.9 , Issue.2 , pp. 243-252
    • Horne, B.1    Hush, D.2
  • 7
    • 0002663413 scopus 로고
    • Turing computability with neural nets
    • H. Siegelmann and E. Sontag, "Turing computability with neural nets," Appl. Math. Lett., vol. 4, no. 6, pp. 77-80, 1991.
    • (1991) Appl. Math. Lett. , vol.4 , Issue.6 , pp. 77-80
    • Siegelmann, H.1    Sontag, E.2
  • 8
    • 0029255891 scopus 로고
    • On the computational power of neural networks
    • _, "On the computational power of neural networks," J. Comput. Syst. Sci.. vol. 50, no. 1, pp. 132-150, 1995.
    • (1995) J. Comput. Syst. Sci. , vol.50 , Issue.1 , pp. 132-150
  • 9
    • 0028500244 scopus 로고
    • Analog computation via neural networks
    • _, "Analog computation via neural networks," Theor. Comput. Sci.. vol. 131, pp. 331-360, 1994.
    • (1994) Theor. Comput. Sci. , vol.131 , pp. 331-360
  • 10
    • 0022011031 scopus 로고
    • Input-output parametric models for nonlinear systems: Part I: deterministic nonlinear systems
    • I. Leontaritis and S. Billings, "Input-output parametric models for nonlinear systems: Part I: deterministic nonlinear systems," Int. J. Control, vol. 41, no. 2, pp. 303-328, 1985.
    • (1985) Int. J. Control , vol.41 , Issue.2 , pp. 303-328
    • Leontaritis, I.1    Billings, S.2
  • 11
    • 0025448276 scopus 로고
    • Non-linear system identification using neural networks
    • S. Chen, S. Billings, and P. Grant, "Non-linear system identification using neural networks," Int. J. Control, vol. 51, no. 6, pp. 1191-1214, 1990.
    • (1990) Int. J. Control , vol.51 , Issue.6 , pp. 1191-1214
    • Chen, S.1    Billings, S.2    Grant, P.3
  • 12
    • 0025399567 scopus 로고
    • Identification and control of dynamical systems using neural networks
    • Mar.
    • K. Narendra and K. Parthasarathy, "Identification and control of dynamical systems using neural networks," IEEE Trans. Neural Networks, vol. 1, pp. 4-27, Mar. 1990.
    • (1990) IEEE Trans. Neural Networks , vol.1 , pp. 4-27
    • Narendra, K.1    Parthasarathy, K.2
  • 13
    • 0026373081 scopus 로고
    • Identification of chemical processes using recurrent networks
    • H.-T. Su and T. McAvoy, "Identification of chemical processes using recurrent networks," in Proc. American Controls Conf., vol. 3, 1991, pp. 2314-2319.
    • (1991) Proc. American Controls Conf. , vol.3 , pp. 2314-2319
    • Su, H.-T.1    McAvoy, T.2
  • 14
    • 0026868901 scopus 로고
    • Long-term predictions of chemical processes using recurrent neural networks: A parallel training approach
    • H.-T. Su, T. McAvoy, and P. Werbos, "Long-term predictions of chemical processes using recurrent neural networks: A parallel training approach," Industrial Engineering and Chemical Research, vol. 31, pp. 1338-1352, 1992.
    • (1992) Industrial Engineering and Chemical Research , vol.31 , pp. 1338-1352
    • Su, H.-T.1    McAvoy, T.2    Werbos, P.3
  • 16
    • 0026626377 scopus 로고
    • Comparison of four neural net learning methods for dynamic system identifcation
    • S.-Z. Qin, H.-T. Su, and T. McAvoy, "Comparison of four neural net learning methods for dynamic system identifcation," IEEE Trans. Neural Networks, vol. 3, no. 1, pp. 122-130, 1992.
    • (1992) IEEE Trans. Neural Networks , vol.3 , Issue.1 , pp. 122-130
    • Qin, S.-Z.1    Su, H.-T.2    McAvoy, T.3
  • 18
    • 0001039722 scopus 로고
    • An experimental comparison of recurrent neural networks
    • G. Tesauro, D. Touretzky, and T. Leen, Eds. Cambridge, MA: MIT Press
    • B. Horne and C. Giles, "An experimental comparison of recurrent neural networks," in Advances in Neural Information Processing Systems 7, G. Tesauro, D. Touretzky, and T. Leen, Eds. Cambridge, MA: MIT Press, 1995, pp. 697-704.
    • (1995) Advances in Neural Information Processing Systems 7 , pp. 697-704
    • Horne, B.1    Giles, C.2
  • 19
    • 0028392483 scopus 로고
    • Learning long-term dependencies with gradient descent is difficult
    • Y. Benigo, P. Simard, and P. Frasconi, "Learning long-term dependencies with gradient descent is difficult," IEEE Trans. Neural Networks, vol. 5, no. 2, pp. 157-166, 1994.
    • (1994) IEEE Trans. Neural Networks , vol.5 , Issue.2 , pp. 157-166
    • Benigo, Y.1    Simard, P.2    Frasconi, P.3
  • 20
    • 33646241633 scopus 로고    scopus 로고
    • Learning long-term dependencies is not as difficult with NARX recurrent neural networks
    • T. Lin. B. Horne, P. Tiňo, and C. Giles, "Learning long-term dependencies is not as difficult with NARX recurrent neural networks," IEEE Trans. Neural Networks, vol. 7, no. 6, 1996.
    • (1996) IEEE Trans. Neural Networks , vol.7 , Issue.6
    • Lin, T.1    Horne, B.2    Tiňo, P.3    Giles, C.4
  • 21
    • 0024861871 scopus 로고
    • Approximation by superpositions of a signoidal function
    • G. Cybenko, "Approximation by superpositions of a signoidal function," Mathematics of Control, Signals, and Systems, vol. 2, no. 4, pp. 303-314, 1989.
    • (1989) Mathematics of Control, Signals, and Systems , vol.2 , Issue.4 , pp. 303-314
    • Cybenko, G.1
  • 22
    • 0024866495 scopus 로고
    • On the approximate realization of continuous mappings by neural networks
    • K. Funahashi, "On the approximate realization of continuous mappings by neural networks," Neural Networks, vol. 2, no. 3, pp. 183-192, 1989.
    • (1989) Neural Networks , vol.2 , Issue.3 , pp. 183-192
    • Funahashi, K.1
  • 25
    • 0029560406 scopus 로고    scopus 로고
    • Learning a class of large finite state machines with a ecurrent neural network
    • C. Giles, B. Horne, and T. Lin, "Learning a class of large finite state machines with a ecurrent neural network," Neural Networks, vol. 8, no. 9, p. 1359, 1996..
    • (1996) Neural Networks , vol.8 , Issue.9 , pp. 1359
    • Giles, C.1    Horne, B.2    Lin, T.3
  • 26
    • 0001460434 scopus 로고
    • The induction of dynamical recognizers
    • J. Pollack, "The induction of dynamical recognizers," Mach. Learn., vol. 7, no. 2/3, pp. 227-252, 1991.
    • (1991) Mach. Learn. , vol.7 , Issue.2-3 , pp. 227-252
    • Pollack, J.1
  • 27
    • 0000029787 scopus 로고
    • FIR and IIR synapses, a new neural network architecture for time series modeling
    • A. Back and A. Tsoi, "FIR and IIR synapses, a new neural network architecture for time series modeling," Neural Computation, vol. 3, no. 3, pp. 375-385, 1991.
    • (1991) Neural Computation , vol.3 , Issue.3 , pp. 375-385
    • Back, A.1    Tsoi, A.2
  • 28
    • 0026895542 scopus 로고
    • The gamma model-A new neural model for temporal processing
    • B. de Vries and J. Principe, "The gamma model-A new neural model for temporal processing," Neural Networks, vol. 5, pp. 565-576, 1992.
    • (1992) Neural Networks , vol.5 , pp. 565-576
    • De Vries, B.1    Principe, J.2
  • 29
    • 0001657855 scopus 로고
    • Local feedback multilayered networks
    • P. Frasconi, M. Gori, and G. Soda, "Local feedback multilayered networks," Neural Computation, vol. 4, pp. 120-130, 1992.
    • (1992) Neural Computation , vol.4 , pp. 120-130
    • Frasconi, P.1    Gori, M.2    Soda, G.3
  • 30
    • 33747998553 scopus 로고    scopus 로고
    • Constructing deterministic finite-state automata in recurrent neural networks
    • C. W. Omlin and C. L. Giles, "Constructing deterministic finite-state automata in recurrent neural networks," J. Assoc. Comput. Mach., 1997.
    • (1997) J. Assoc. Comput. Mach.
    • Omlin, C.W.1    Giles, C.L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.