Hybrid fuzzy convolution modelling and identification of chemical process systems

International Journal of Systems Science

Volumn 31, Issue 4, 2000, Pages 457-466

  Abonyi, János ^a   Nagy, Lajos ^a   Szeifert, Ferenc ^a  

^a UNIVERSITY OF PANNONIA   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL ENGINEERING; COMPUTER SIMULATION; FUZZY CONTROL; MATHEMATICAL MODELS; PROCESS ENGINEERING;

CHEMICAL PROCESS SYSTEMS;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 0034175342     PISSN: 00207721     EISSN: 14645319     Source Type: Journal    
DOI: 10.1080/002077200291046     Document Type: Article

References (22)

1. Babuska, R 1997, Fuzzy modelling and identification. PhD Thesis, Delft University of Technology.
2. Bhat, N and McAvoy T. J 1990, Use of neural nets for dynamic modelling and control of chemical process systems. Computers and Chemical Engineering, 14, 573-583.
3. Bhat, N., Minderman, P. a., McAvoy, T. J., and Wang, J., 1990, Modeling chemical process systems via neural computaion. IEEE Control Systems Magazine, April, 24-30.
4. Bossley, K. M., 1997, Neurofuzzy modelling approaches in system identi® cation. PhD Thesis, University of Southampton.
5. Brown, M., Bossley, K. M., Mills, D. J., and Harris, C. J., 1995, High dimensional neurofuzzy systems: overcoming the course of dimensionality. Proceedings of the International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and the Second International Fuzzy Engineering Symposium, Yokohama, Japan, pp. 2139-2146.
6. Cox, M. G., 1982, Practical spline approximation. Technical Report DITC 1/82, National Physical Laboratory, 33 pp.
7. Duwaish, H. Al., and Karim, N. M., 1997, A new method for identification of Hammerstein model. Automatica, 33, 1871-1875.
8. Duwaish, H. Al., Karim, N. M., and Chandrasekar, V., 1996, Use of multilayer feedforward neural networks in identi cation and control of Wiener model. Proceedings of the Institution of Electrical Engineers, Pt D, 143, 255-258.
9. Eskinat, E., Johnson, S. H., and Luyben, W., 1991, Use of Hammerstein models in identification of non-linear systems. AIChE Journal, 37, 255-268.
10. Grace, A., 1994, Optimization Toolbox For Use with MATLAB (MathWorks Inc.).
11. Leontaritis, I. J., and Billings, S. A., 1987, Experimental design and identi® ably for non-linear systems. International Journal of Systems Science, 18, 189-202.
12. McAvoy, T. J., Hsu, E., and Lowenthal, S., 1972, Dynamics of pH in controlled stirred tank reactor. Industrial and Engineering Chemistry, Process Design and Development, 11, 68-70.
13. Mohilla, R., and Ferencz, B., 1982, Chemical Process Dynamics, Fundamental Studies in Engineering, Vol. 4 (Amsterdam: Elsevier).
14. Narendra, K. S., and Parthasarathy, K., 1990, Identification and control of dynamical systems. IEEE Transactions on Neural Networks, 1, 4-27.
15. Pottman, M., and Pearson, R. K., 1998, Block-oriented NARMAX models with output multiplicities. AIChE Journal, 44, 131-140.
16. Psichogios, D. C., and Ungar, L. H., 1992, A hybrid neural network first principles approach to process modeling. AIChE Journal, 38, 1499-1511.
17. Ramchandran, S., 1998, Consider steady-state models for process control. Chemical Engineering Progress, February, 75-81.
18. Ricker, N. L., 1988, The use of biased least-squares estimators for parameters in discrete-time pulse response models. Industrial and Engineering Chemistry Research, 27, 343-350.
19. Schubert, j., 1994, Bioprocess optimization and control: application of hybrid modelling. Journal of Biotechnology, 35, 51-68.
20. Takagi, T., and Sugeno, M., 1985, Fuzzy identification of systems and its application to modelling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15, 116-132.
21. Thompson, M., and Kramer, M. A., 1994, Modeling chemical processes using prior knowledge and neural networks. AIChE Journal, 40, 1328-1340.
22. Wahlberg, B., 1991, System identification using Kautz models. IEEE Transactions on Automatic Control, 39, 1276-1282.