Practical identification of narmax models using radial basis functions

International Journal of Control

Volumn 52, Issue 5, 1990, Pages 1327-1350

  Chen, S ^a   Billings, S A ^b   Cowan, C F ^b   Grant, P M ^b  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMOBILE ENGINES - DIESEL ENGINES; COMPUTER PROGRAMMING - ALGORITHMS; CONTROL SYSTEMS, DISCRETE TIME; LIQUID LEVEL INDICATORS; STATISTICAL METHODS - REGRESSION ANALYSIS;

AUTOREGRESSIVE MOVING AVERAGE MODELS; RADIAL BASIS FUNCTIONS;

CONTROL SYSTEMS, NONLINEAR;

EID: 0025669403     PISSN: 00207179     EISSN: 13665820     Source Type: Journal    
DOI: 10.1080/00207179008953599     Document Type: Article

References (18)

1. BILLINGS, S. A., and CHEN, S., 1989, Identification of non-linear rational systems using a prediction-error estimation algorithm. Internal Journal of Systems Science, 20, 467-494; 1989
2. BILLINGS, S. A., CHEN, S., and BACKHOUSE, R. I., 1989, The identification of linear and non- linear models of a turbocharged automotive diesel engine. Mechanical Systems and Signal Processing, 3, 123-142.
3. BILLINGS, S. A., CHEN, S., and KORENBERG, M. J., 1989, Identification of MIMO non-linear systems using a forward-regression orthogonal estimator. International Journal of Control, 49, 2157-2189.
4. BILLINGS, S. A., and VOON, W. S. F., 1986, Correlation based model validity tests for non- linear models. International Journal of Control, 44, 235-244.
5. BJORCK, A., 1967, Solving linear least squares problems by Gram-Schmidt orthogonalization. Nordisk. Tidskr. Informations-Behandling, 7, 1-21
6. BOHLIN, T., 1987, Maximum-power validation of models without higher-order fitting. Automatica, 4, 137-146.
7. BROOMHEAD, D.S., and LOWE, D., 1988, Multivariable functional interpolation and adaptive networks. Complex Systems, 2, 321-355.
8. CHEN, S., and BILLINGS, S. A., 1988, Prediction-error estimation algorithm for non-linear output-affine systems. International Journal of Control, 47, 309-332;
9. CHEN, S., BILLINGS, S. A., and LUO, W., 1989, Orthogonal least squares methods and their application to non-linear system identification. International Journal of Control, 50, 1873-1896.
10. CHEN, S., BILLINGS, S. A., COWAN, C. F. N., and GRANT, P. M., 1991, Non-linear systems identification using radial basis functions. International Journal of Systems Science, to be published.
11. CYBENKO, G., 1989, Approximations by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems, 2, 303-314.
12. GOLUB, G., 1965, Numerical methods for solving linear least squares problems. Numerische Mathematik, 7, 206-216.
13. LAPEDES, A., and FARBER, R., 1988, How neural nets work. Neural Information Processing Systems, edited by D. Z. Anderson (New York: American Institute of Physics), pp. 442-456.
14. LEONTARITIS, I. J., and BILLINGS, S. A., 1985, Input-output parametric models for non-linear systems Part I: Deterministic non-linear systems; Part 2: Stochastic non-linear systems. International Journal of Control, 41, 303-344;
15. LIPPMANN, R. P., 1987, An introduction to computing with neural nets. I.E.E.E. ASSP Magazine, 4.
16. MICCHELLI, C. A., 1986, Interpolation of scattered data: distance matrices and conditionally positive definite functions. Constructive Approximation, 2, 11-22.
17. POWELL, M. R. D., 1985, Radial basis functions for multi variable interpolation: a review. 1M A Conference on Algorithms for the Approximation of Functions and Data, RMCS, pp. 223-241.
18. SONTAG, E. D., 1979, Polynomial Response Maps, Lecture Notes in Control and Information Science, Vol. 13 (Berlin: Springer-Verlag).