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Volumn 1, Issue 3, 1995, Pages 247-258

Nonlinear system approximation by neural networks: Error stability analysis

Author keywords

Dynamic systems; Lyapunov like analysis; Neural network; Nonlinear systems; Stability analysis

Indexed keywords


EID: 0005616541     PISSN: 10798587     EISSN: 2326005X     Source Type: Journal    
DOI: 10.1080/10798587.1995.10750633     Document Type: Article
Times cited : (39)

References (15)
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    • Narendra, K.S.1    Parthasarathy, K.2
  • 3
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    • Neural Networks for Control Systems-A Survey
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    • Hunt, K.J., D. Sbarbaro, R. Zbikowsky, and P.J. Gawthrop. “Neural Networks for Control Systems-A Survey.” Automatica, Vol. 28, No 6 (December, 1992), pp. 1083-1192.
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    • Hunt, K.J.1    Sbarbaro, D.2    Zbikowsky, R.3    Gawthrop, P.J.4
  • 5
    • 0004469897 scopus 로고
    • Neurons with Graded Response have Collective Properties like Those of Two-Sates Neurons
    • Hopfield, J.J. “Neurons with Graded Response have Collective Properties like Those of Two-Sates Neurons.” Proc. Nat’l. Acad. of Scien., 81 (May 1984), pp. 3088-3092.
    • (1984) Proc. Nat’l. Acad. Of Scien , vol.81 , pp. 3088-3092
    • Hopfield, J.J.1
  • 6
    • 0020970741 scopus 로고
    • Absolute Stability of Global Pattern Formation and Parallel Memory Storage by Competitive Neural Networks
    • Sept./Oct
    • Cohen, M.A., and S. Grossberg. “Absolute Stability of Global Pattern Formation and Parallel Memory Storage by Competitive Neural Networks.” IEEE Trans. on Syst., Man, and Cybern., Vol.13, No.5 (Sept./Oct. 1983), pp. 815-826.
    • (1983) IEEE Trans. On Syst., Man, and Cybern , vol.13 , Issue.5 , pp. 815-826
    • Cohen, M.A.1    Grossberg, S.2
  • 8
    • 0027851970 scopus 로고
    • State Observability in Recurrent Neural Networks
    • December
    • Albertini, F. and E. D. Sontag. “State Observability in Recurrent Neural Networks,” in Proc. 32nd IEEE CDC, December 1993.
    • (1993) Proc. 32Nd IEEE CDC
    • Albertini, F.1    Sontag, E.D.2
  • 10
    • 0027154319 scopus 로고
    • Approximation of Dynamical Systems by Continuous Time Recurrent Neural Networks
    • Funahashi, K., and Y. Nakamura. “Approximation of Dynamical Systems by Continuous Time Recurrent Neural Networks.” Neural Networks, Vol. 6 (1993), pp. 801-806.
    • (1993) Neural Networks , vol.6 , pp. 801-806
    • Funahashi, K.1    Nakamura, Y.2
  • 11
    • 0026156360 scopus 로고
    • Approximation Theorems for Discrete-Time Systems
    • Sandberg, I.W. “Approximation Theorems for Discrete-Time Systems.” IEEE Trans. on Circ. and Syst., Vol. 38 (1991), pp. 564-566.
    • (1991) IEEE Trans. On Circ. And Syst , vol.38 , pp. 564-566
    • Sandberg, I.W.1
  • 12
    • 0027680863 scopus 로고
    • Uniform Approximation and the Circle Criterion
    • Sandberg, I.W. “Uniform Approximation and the Circle Criterion.” IEEE Trans. on Automatic Control, Vol. 38 (1992), pp. 1450-1458.
    • (1992) IEEE Trans. On Automatic Control , vol.38 , pp. 1450-1458
    • Sandberg, I.W.1
  • 14
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    • Dynamic Neural Network for Nonlinear Systems Identification
    • December
    • Sanchez, E.N. “Dynamic Neural Network for Nonlinear Systems Identification,” in Proc. 33rd IEEE CDC, December 1994.
    • (1994) Proc. 33Rd IEEE CDC
    • Sanchez, E.N.1
  • 15
    • 0015206454 scopus 로고
    • Least Squares Optimal Control and Algebraic Riccati Equations
    • Willems, J.C. “Least Squares Optimal Control and Algebraic Riccati Equations.” IEEE Trans. on Automatic Control, Vol. 16, No 6 (1971), pp. 621-634.
    • (1971) IEEE Trans. On Automatic Control , vol.16 , Issue.6 , pp. 621-634
    • Willems, J.C.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.