RBFN identification of a solution copolymerization model

Proceedings of the American Control Conference

Volumn 5, Issue , 1998, Pages 3182-3188

  Bomberger, John D ^a,b   Seborg, Dale E ^b  

^a University of California   (United States)

^b DUPONT COMPANY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE GRADIENT; FALSE NEAREST NEIGHBOR; INPUT SEQUENCE; MODEL ORDER DETERMINATION; RADIAL BASIS FUNCTION NETWORKS (RBFN); SOLUTION COPOLYMERIZATION; STEPWISE REGRESSION ANALYSIS;

RADIAL BASIS FUNCTION NETWORKS;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 84881341835     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACC.1998.688449     Document Type: Conference Paper

References (17)

1. Abarbanel, H. D. I., R. Brown, J. J. Sidorowich, and L. S. Tsimring, Analysis of observed chaotic data in physical systems. Rev. Mod. Phys., 65, 1331-1392 (1993)
2. Akaike, H., Information theory and an extension to the maximum likelihood principle. In 2nd Int. Symp. on Information Theory, Tsahkador, Armenia, USSR, Sept. 2-8, 1971, B. N. Petrov and F. Csáki, eds. Akadémiai Kiadó, Budapest, 267-281 (1973).
3. Banerjee, A., Y. Arkun, B. Ogunnaike, and R. Pearson, Estimation of nonlinear systems using linear multiple models. AI ChE J., 43, 1204-1226 (1997).
4. Bomberger, J. D., Radial Basis Function Networks for Process Identification. Ph.D. Dissertation, University of California, Santa Barbara (1997).
5. Bomberger, J. D., G. Lightbody, D. E. Seborg, and G. W. Irwin, Experimental evaluation of neural nonlinear modeling techniques. Preprints Chemical Process Control V, Jan. 7-12, Tahoe City, CA (1996).
6. Bomberger, J. D. and D. E. Seborg, Estimation of model order from input-output data applied to radial basis function network identification. Preprints IFAC-ADCHEM '97, June 9-11, Banff, Canada, 31-36 (1997a).
7. Bomberger, J. D. and D. E. Seborg, Determination of the width parameter for Gaussian radial basis function networks using approximate gradient norms. To be presented at AIChE Annual Meeting, Nov. 16-21, Los Angeles (1997b).
8. Congalidis, J. P., J. R. Richards, and W. H. Ray, Feedforward and feedback control of a solution copolymerization reactor. AIChE J., 35, 891-907 (1989).
9. Draper, N. R. and H. Smith, Applied Regression Analysis. 2nd. Ed. John Wiley, New York (1981).
10. Eikens, B. and M. N. Karim, Real-time control of a waste water neutralization process using radial basis functions. Preprints IFAC-ADCHEM '94, May 25-27, Kyoto, Japan, 125-130 (1994).
11. Kennel, M. B., R. Brown, and H. D. I. Abarbanel, Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A, 45, 3403-3411 (1992).
12. Keulers, M., Structure and parameter identification of a batch fermentation process using non-linear modelling. Proc. ACC, June 2-4, San Francisco, CA, 2261-2265 (1993).
13. Moody, J. and C. J. Darken, Fast learning in networks of locally-tuned processing units. Neural Computation, 1, 281-294 (1989).
14. Pottmann, M. and D. E. Seborg, Identification of nonlinear processes using reciprocal multiquadric functions. J. Process Control, 2, 189-203 (1992).
15. Sjöberg, J., Q. Zhang, L. Ljung, A. Benveniste, B. Deylon, P.-Y. Glorennec, H. Hjalmarsson, and A. Juditsy, Nonlinear black-box modeling in system identification: a unified overview. Automatica, 31, 1691-1724 (1995).
16. Rhodes, C. and M. Morari, Determining the model order of nonlinear input/output systems directly from data. Proc. of the ACC, June 21-23, Seattle, WA, 2190-2194 (1995).
17. Rhodes, C. and M. Morari, The false nearest neighbors algorithm: an overview. Computers Chem. Engng., 21, Suppl., S1149-S1154 (1997).