Comment on 'A quasi-ARMAX approach to the modelling of non-linear systems' by J. Hu et al.

International Journal of Control

Volumn 74, Issue 18, 2001, Pages 1767-1771

  Young, Peter ^a  

^a LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACTUATORS; COMPUTER SIMULATION; DATABASE SYSTEMS; FUZZY CONTROL; NEURAL NETWORKS; PARAMETER ESTIMATION; POLYNOMIALS; ROBOTIC ARMS; ROBUSTNESS (CONTROL SYSTEMS); STOCHASTIC CONTROL SYSTEMS;

DATA-BASED MECHANISTIC (DBM); NEURO-FUZZY MODELLING;

NONLINEAR CONTROL SYSTEMS;

EID: 0035895107     PISSN: 00207179     EISSN: None     Source Type: Journal    
DOI: 10.1080/00207170110089789     Document Type: Article

References (7)

1. Hu, J., Kumamaru, K., and Hirasawa, K., 2001, A quasi-ARMAX approach to modelling nonlinear systems. International Journal of Control, 74, 1754-1766.
2. Jang, J.-S. R., Sun, C.-T., and Mizutani, E., 1997, Neuro-Fuzzy and Soft Computing, (Upper Daddle River, NJ: Prentice-Hall).
3. Sjöberg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorrenec, P. Y., Hjalmarsson, H., and Juditsky, A., 1995, Nonlinear black-box modelling in system identification: a unified overview. Automatica, 31, 1691-1724.
4. Young, P. C., 2001, Data-based mechanistic modelling of enviromnental systems. Plenary Session Paper, IFA C Workshop on Modeling and Control of Environmental Systems, Yokohama, Japan.
5. Young, P. C., and Jakeman, A. J., 1979, Refined instrumental variable methods of recursive time series analysis, Part I: single input, single output systems. International Journal of Control, 29, 1-30.
6. Young, P. C., Mckenna, P., and Bruun, J., 2001, Identification of nonlinear stochastic systems by state dependent parameter estimation, this Special Issue.
7. Young, P. C., Price, L., Berckmans, D., and Janssens, K., 2000, Recent developments in the modelling of imperfectly mixed airspaces. Computers and Electronics in Agriculture, 26, 239-254.