메뉴 건너뛰기




Volumn 12, Issue 3, 2000, Pages 291-303

Nonlinear system identification using Lyapunov based fully tuned dynamic RBF networks

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; LYAPUNOV METHODS; NONLINEAR CONTROL SYSTEMS; SYSTEM STABILITY;

EID: 0034498806     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1026571426761     Document Type: Article
Times cited : (20)

References (9)
  • 1
    • 0030392234 scopus 로고    scopus 로고
    • Nonlinear dynamic system identification using radial basis function networks
    • Kobe, Japan
    • Ni, X. and Simons, Stef J. R.: Nonlinear dynamic system identification using radial basis function networks. Proceedings of the 35th Conference on Decision and Control, Kobe, Japan, (1996), pp. 935-936.
    • (1996) Proceedings of the 35th Conference on Decision and Control , pp. 935-936
    • Ni, X.1    Simons, S.J.R.2
  • 2
    • 0030244458 scopus 로고    scopus 로고
    • Stable sequential identification of continuous nonlinear dynamical systems by growing radial basis function networks
    • Liu, G. P., Kadirkamanathan, V. and Billings, S. A.: Stable sequential identification of continuous nonlinear dynamical systems by growing radial basis function networks. Int. J. Control, 65(1) (1996), 53-69.
    • (1996) Int. J. Control , vol.65 , Issue.1 , pp. 53-69
    • Liu, G.P.1    Kadirkamanathan, V.2    Billings, S.A.3
  • 3
    • 0027632249 scopus 로고
    • A clustering technique for digital communications channel equilization using radial basis function networks
    • Chen, S., Mulgrew, B. and Grant, P. M.: A clustering technique for digital communications channel equilization using radial basis function networks. IEEE Trans. Neural Networks 4 (1993), 570-579.
    • (1993) IEEE Trans. Neural Networks , vol.4 , pp. 570-579
    • Chen, S.1    Mulgrew, B.2    Grant, P.M.3
  • 5
    • 0029489301 scopus 로고
    • A fully Kalman-trained radial basis function network for nonlinear speech modeling
    • Birgmeier, M.: A fully Kalman-trained radial basis function network for nonlinear speech modeling. Proc. IEEE Int. Conf. Neural Networks 1 (1995), 259-264.
    • (1995) Proc. IEEE Int. Conf. Neural Networks , vol.1 , pp. 259-264
    • Birgmeier, M.1
  • 6
    • 0032022388 scopus 로고    scopus 로고
    • Performance evolution of a sequential minimal RBF neural network learning algorithm
    • Lu, Y., Sundararajan, N. and Saratchandran, P.: Performance evolution of a sequential minimal RBF neural network learning algorithm. IEEE Trans. on Neural Networks 9(2) (1998), 308-318.
    • (1998) IEEE Trans. on Neural Networks , vol.9 , Issue.2 , pp. 308-318
    • Lu, Y.1    Sundararajan, N.2    Saratchandran, P.3
  • 7
    • 0032777345 scopus 로고    scopus 로고
    • On the Kalman filtering method in neural-network training and pruning
    • Sum, J., Chi-Sing Leung, Young, G. H. and Wing-Kay Kan.: On the Kalman filtering method in neural-network training and pruning. IEEE Trans. on Neural Networks 10(1) (1999), 161-166.
    • (1999) IEEE Trans. on Neural Networks , vol.10 , Issue.1 , pp. 161-166
    • Sum, J.1    Leung, C.-S.2    Young, G.H.3    Kan, W.-K.4
  • 8
    • 0000106040 scopus 로고
    • Universal approximation using radial basis function networks
    • Park, J. and Sandberg, I. W.: Universal approximation using radial basis function networks. Neural Computation 3 (1991), 246-257.
    • (1991) Neural Computation , vol.3 , pp. 246-257
    • Park, J.1    Sandberg, I.W.2
  • 9
    • 0001071040 scopus 로고
    • A resource allocating network for function interpolation
    • Platt, J. C.: A resource allocating network for function interpolation. Neural Computation 3(1991), 213-225.
    • (1991) Neural Computation , vol.3 , pp. 213-225
    • Platt, J.C.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.