메뉴 건너뛰기




Volumn 10, Issue 1, 1998, Pages 165-188

A Low-Sensitivity Recurrent Neural Network

Author keywords

[No Author keywords available]

Indexed keywords


EID: 0346966878     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976698300017935     Document Type: Article
Times cited : (3)

References (38)
  • 1
    • 0016622069 scopus 로고
    • New recursive digital filter structures having very low sensitivity and roundoff noise
    • Agarwal, R., & Burrus, C. (1975). New recursive digital filter structures having very low sensitivity and roundoff noise. IEEE Trans. Circuits, Syst., CAS-22(12), 921-927.
    • (1975) IEEE Trans. Circuits, Syst. , vol.CAS-22 , Issue.12 , pp. 921-927
    • Agarwal, R.1    Burrus, C.2
  • 3
    • 0021195351 scopus 로고
    • Zeros of sampled systems
    • Astrom, K., Hagander, P., & Sternby, J. (1984). Zeros of sampled systems. Automatica, 20(1), 31-38.
    • (1984) Automatica , vol.20 , Issue.1 , pp. 31-38
    • Astrom, K.1    Hagander, P.2    Sternby, J.3
  • 4
    • 85153962684 scopus 로고
    • A comparison of discrete-time operator models for nonlinear system identification
    • D. S. T. G. Tesauro & T. K. Leen (Eds.), Cambridge, MA: MIT Press
    • Back, A. D, & Tsoi, A.-C. (1994). A comparison of discrete-time operator models for nonlinear system identification. In D. S. T. G. Tesauro & T. K. Leen (Eds.), Advances in neural information processing systems 7 (pp. 883-890). Cambridge, MA: MIT Press.
    • (1994) Advances in Neural Information Processing Systems , vol.7 , pp. 883-890
    • Back, A.D.1    Tsoi, A.-C.2
  • 5
    • 0029236017 scopus 로고
    • Constrained pole-zero filters as discrete-time operators for system approximation
    • E. M. F. Girosi, J. Makhoul, & E. Wilson (Eds.), New York: IEEE Press
    • Back, A. D., & Tsoi, A.-C. (1995). Constrained pole-zero filters as discrete-time operators for system approximation. In E. M. F. Girosi, J. Makhoul, & E. Wilson (Eds.), Proc. of the 1995 IEEE Workshop Neural Networks for Signal Processing 5 (NNSP95) (pp. 191-200). New York: IEEE Press.
    • (1995) Proc. of the 1995 IEEE Workshop Neural Networks for Signal Processing 5 (NNSP95) , pp. 191-200
    • Back, A.D.1    Tsoi, A.-C.2
  • 6
    • 0028720734 scopus 로고
    • A unifying view of some training algorithms for multilayer perceptrons with FIR filter synapses
    • J. H. J. Vlontzos & E. Wilson (Eds.), New York: IEEE Press
    • Back, A. D, Wan, E., Lawrence, S., & Tsoi, A.-C. (1994). A unifying view of some training algorithms for multilayer perceptrons with FIR filter synapses. In J. H. J. Vlontzos & E. Wilson (Eds.), Proc. of the 1994 IEEE Workshop Neural Networks for Signal Processing 4 (NNSP94) (pp. 146-154). New York: IEEE Press.
    • (1994) Proc. of the 1994 IEEE Workshop Neural Networks for Signal Processing 4 (NNSP94) , pp. 146-154
    • Back, A.D.1    Wan, E.2    Lawrence, S.3    Tsoi, A.-C.4
  • 7
    • 0026625982 scopus 로고
    • Sensitivity analysis of multilayer perceptrons with differentiable activation functions
    • Choi, J., & Choi, C.-H. (1992). Sensitivity analysis of multilayer perceptrons with differentiable activation functions. IEEE Trans. Neural Networks, 3, 101-107.
    • (1992) IEEE Trans. Neural Networks , vol.3 , pp. 101-107
    • Choi, J.1    Choi, C.-H.2
  • 9
    • 0026895542 scopus 로고
    • The Gamma model - A new neural model for temporal processing
    • de Vries, B., & Principe, J. (1992). The Gamma model - a new neural model for temporal processing. Neural Networks, 5(4), 565-576.
    • (1992) Neural Networks , vol.5 , Issue.4 , pp. 565-576
    • De Vries, B.1    Principe, J.2
  • 10
    • 0029410181 scopus 로고
    • The effects of quantization on multilayer perceptrons
    • Dündar, G., & Rose, K. (1995). The effects of quantization on multilayer perceptrons. IEEE Trans. Neural Networks, 6(6), 1446-1451.
    • (1995) IEEE Trans. Neural Networks , vol.6 , Issue.6 , pp. 1446-1451
    • Dündar, G.1    Rose, K.2
  • 11
    • 0027189264 scopus 로고
    • A δ-operator recursive gradient algorithm for adaptive signal processing
    • New York: IEEE Press
    • Fan, H., & Li, Q. (1993). A δ-operator recursive gradient algorithm for adaptive signal processing. In Proc. IEEE Int. Conf. Acoust. Speech, Signal Proc. (Vol. III, pp. 492-495). New York: IEEE Press.
    • (1993) Proc. IEEE Int. Conf. Acoust. Speech, Signal Proc. , vol.3 , pp. 492-495
    • Fan, H.1    Li, Q.2
  • 14
    • 0026820882 scopus 로고
    • High-speed digital signal processing and control
    • Goodwin, G., Middleton, R., & Poor, H. (1992). High-speed digital signal processing and control. Proc. IEEE, 80(2), 240-259.
    • (1992) Proc. IEEE , vol.80 , Issue.2 , pp. 240-259
    • Goodwin, G.1    Middleton, R.2    Poor, H.3
  • 15
    • 0029263844 scopus 로고
    • A generalized orthonormal basis for linear dynamical systems
    • Heuberger, P., Bosgra, O., & Van den Hof, P. (1995). A generalized orthonormal basis for linear dynamical systems. IEEE Trans. Automat. Control, AC-40(3), 451-165.
    • (1995) IEEE Trans. Automat. Control , vol.AC-40 , Issue.3 , pp. 451-1165
    • Heuberger, P.1    Bosgra, O.2    Van Den Hof, P.3
  • 16
    • 0029304670 scopus 로고
    • Theoretical investigation of the robustness of multilayer perceptrons: Analysis of the linear case and extension to nonlinear networks
    • Kerlirzin, P. & Réfrégier, P. (1995). Theoretical investigation of the robustness of multilayer perceptrons: Analysis of the linear case and extension to nonlinear networks. IEEE Trans. Neural Networks, 6, 560-571.
    • (1995) IEEE Trans. Neural Networks , vol.6 , pp. 560-571
    • Kerlirzin, P.1    Réfrégier, P.2
  • 17
    • 0005553173 scopus 로고
    • Eigenvalue sensitivity and state-variable selection
    • Mantey, P. (1968). Eigenvalue sensitivity and state-variable selection. IEEE Trans. Automat. Control, AC-13(3), 263-269.
    • (1968) IEEE Trans. Automat. Control , vol.AC-13 , Issue.3 , pp. 263-269
    • Mantey, P.1
  • 18
    • 0342471393 scopus 로고    scopus 로고
    • Neural networks and approximation theory
    • Mhaskar, H. (1996). Neural networks and approximation theory. Neural Networks, 9(4), 721-722.
    • (1996) Neural Networks , vol.9 , Issue.4 , pp. 721-722
    • Mhaskar, H.1
  • 20
    • 0027928976 scopus 로고
    • Perturbation response in feedforward networks
    • Minai, A., & Williams, R. (1994). Perturbation response in feedforward networks. Neural Networks, 7(5), 783-796.
    • (1994) Neural Networks , vol.7 , Issue.5 , pp. 783-796
    • Minai, A.1    Williams, R.2
  • 21
    • 0029341842 scopus 로고
    • Sensitivity analysis of single hidden-layer neural networks with threshold functions
    • Oh, S.-H., & Lee, Y. (1995). Sensitivity analysis of single hidden-layer neural networks with threshold functions. IEEE Trans. Neural Networks, 6, 1005-1007.
    • (1995) IEEE Trans. Neural Networks , vol.6 , pp. 1005-1007
    • Oh, S.-H.1    Lee, Y.2
  • 22
    • 0021468581 scopus 로고
    • Low-sensitivity recursive digital filters obtained via the delay replacement
    • Orlandi, G., & Martinelli, G. (1984). Low-sensitivity recursive digital filters obtained via the delay replacement. IEEE Trans. Circuits, Syst., CAS-31, 654-657.
    • (1984) IEEE Trans. Circuits, Syst. , vol.CAS-31 , pp. 654-657
    • Orlandi, G.1    Martinelli, G.2
  • 23
    • 84943259479 scopus 로고
    • Performance of multilayer neural networks in binary-to-binary mappings under weight errors
    • New York: IEEE Press
    • Orzechowski, N., Kumara, S., & Das, C. (1993). Performance of multilayer neural networks in binary-to-binary mappings under weight errors. In Proc. ICNN93, San Francisco (pp. 1684-1689). New York: IEEE Press.
    • (1993) Proc. ICNN93, San Francisco , pp. 1684-1689
    • Orzechowski, N.1    Kumara, S.2    Das, C.3
  • 25
    • 0027542363 scopus 로고
    • The Gamma filter - A new class of adaptive IIR filters with restricted feedback
    • Principe, J., de Vries, B., & de Oliveira, P. G. (1993). The Gamma filter - a new class of adaptive IIR filters with restricted feedback. IEEE Trans. Signal Processing, 41, 649-656.
    • (1993) IEEE Trans. Signal Processing , vol.41 , pp. 649-656
    • Principe, J.1    De Vries, B.2    De Oliveira, P.G.3
  • 29
  • 30
    • 0025404853 scopus 로고
    • Sensitivity analysis of feedforward neural networks to weight errors
    • Stevenson, M., Winter, R., & Widrow, B. (1990). Sensitivity analysis of feedforward neural networks to weight errors. IEEE Trans. Neural Networks, 1, 71-90.
    • (1990) IEEE Trans. Neural Networks , vol.1 , pp. 71-90
    • Stevenson, M.1    Winter, R.2    Widrow, B.3
  • 32
    • 84975563420 scopus 로고
    • Influence of interconnection weight discretization and noise in an optoelectronic neural network
    • Von Lehman, A., Paek, E., Liao, P., Marrakchi, A., & Patel, J. (1989). Influence of interconnection weight discretization and noise in an optoelectronic neural network. Optics Letters, 14, 928-930.
    • (1989) Optics Letters , vol.14 , pp. 928-930
    • Von Lehman, A.1    Paek, E.2    Liao, P.3    Marrakchi, A.4    Patel, J.5
  • 33
    • 0026157695 scopus 로고
    • System identification using Laguerre models
    • Wahlberg, B. (1991). System identification using Laguerre models. IEEE Trans. Automat. Control, 36, 551-562.
    • (1991) IEEE Trans. Automat. Control , vol.36 , pp. 551-562
    • Wahlberg, B.1
  • 34
    • 0028272551 scopus 로고
    • Robustness and perturbation analysis of a class of artificial neural networks
    • Wang, K., & Michel, A. (1994). Robustness and perturbation analysis of a class of artificial neural networks. Neural Networks, 7(2), 251-259.
    • (1994) Neural Networks , vol.7 , Issue.2 , pp. 251-259
    • Wang, K.1    Michel, A.2
  • 35
    • 0001202594 scopus 로고
    • A learning algorithm for continually running fully recurrent neural networks
    • Williams, R., & Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1, 270-280.
    • (1989) Neural Computation , vol.1 , pp. 270-280
    • Williams, R.1    Zipser, D.2
  • 37
    • 0028733792 scopus 로고
    • Calculation of the Volterra kernels of non-linear dynamic sytems using an artificial neural network
    • Wray, J., & Green, G. (1994). Calculation of the Volterra kernels of non-linear dynamic sytems using an artificial neural network. Biological Cybernetics, 71, 187-195.
    • (1994) Biological Cybernetics , vol.71 , pp. 187-195
    • Wray, J.1    Green, G.2
  • 38
    • 0026836759 scopus 로고
    • Analysis of the effects of quantization in multilayer neural networks using a statistical model
    • Xie, Y., & Jabri, M. (1992). Analysis of the effects of quantization in multilayer neural networks using a statistical model. IEEE Trans. Neural Networks, 3, 334-338.
    • (1992) IEEE Trans. Neural Networks , vol.3 , pp. 334-338
    • Xie, Y.1    Jabri, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.