Modular neural network modelling for long-range prediction of an evaporator

Control Engineering Practice

Volumn 8, Issue 1, 2000, Pages 49-59

  Russell, N T ^b   Bakker, H H C ^a   Chaplin, R I ^a  

^a MASSEY UNIVERSITY   (New Zealand)

^b Predictive Control Ltd   (United Kingdom)

Author keywords

Evaporators; Model based predictive control; Modular modelling; Neural networks; Prediction; Simulation

Indexed keywords

COMPUTER SIMULATION; EVAPORATORS; PREDICTIVE CONTROL SYSTEMS;

MODULAR NEURAL NETWORK MODELS;

NEURAL NETWORKS;

EVAPORATOR; NEURAL NETWORK; PREDICTION MODEL;

EID: 0034023490     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0967-0661(99)00123-9     Document Type: Article

References (18)

1. Gallinari P. Training of modular neural net systems. Arbib M.A. The handbook of brain theory and neural networks. 1995;582-585 MIT Press, MA, USA.
2. Hagan M.T., Menhaj M.B. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks. 5:1994;989-993.
3. Hunt K.J., Sbarbaro D., Zbikowski R., Gawthrop P.J. Neural networks for control systems - a survey. Automatica. 28:1992;1083-1112.
4. Levenberg K. A method for the solution of certain nonlinear problems in least squares. Quarterly of Applied Mathematics. 2:1944;164-168.
5. Ljung L. System identification - Theory for the user. 1987;Prentice-Hall, Englewood Cliffs, NJ, USA.
6. Marquardt D.W. An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society of Industrial Applications Math. 11:1963;431-441.
7. Mavrovouniotis M.L., Chang S. Hierarchical neural networks. Computers and Chemical Engineering. 16:1992;347-369.
8. Morris A.J., Montague G.A., Willis M.J. Artificial neural networks. Studies in process modelling and control Transactions of the Institution of Chemical Engineers. 72:1994;3-19.
9. Narendra K.S., Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks. 1:1990;4-27.
10. Pearlmutter, B.A. (1990). Dynamic recurrent neural networks. Technical Report CMU-CS-90-196, Carnegie Mellon University, Pittsburgh, PA.
11. Quaak P., Gerritsen J.B.M. Modelling dynamic behaviour of multiple-effect falling-film evaporators. Bussemaker H.T., Iedema P.D. Computer applications in chemical engineering. 1990;59-64 Elsevier, Amsterdam.
12. Rumelhart D.E., Hinton G.E., Williams R.J. Learning internal representations by error propagation. Rumelhart D.E., McClelland J.L. Parallel distributed processing. vol 1:1986;318-362 MIT Press, Cambridge, MA, USA.
13. Russell. N.T. (1997). Dynamic modelling of a falling-film evaporator for model predictive control. Ph.D. thesis, Massey University, New Zealand.
14. Russell N.T., Bakker H.H.C. Modular modelling of an evaporator for long-range prediction. Artificial Intelligence in Engineering. 11:1997;347-355.
15. Su H.-T., McAvoy J., Werbos P. Long-term predictions of chemical processes using recurrent neural networks. A parallel training approach Industrial Engineering Chemistry Research. 31:1992;1338-1352.
16. Werbos P.J. Backpropagation through time. what it is and how to do it Proceedings of the IEEE. 78:1990;1550-1560.
17. Williams R.J., Zipser D. A learning algorithm for continually running fully recurrent neural networks. Neural Computation. 1:1989;270-280.
18. Willis M.J., Di Massimo C., Montague G.A., Tham M.T., Morris A.J. Artificial neural networks in process engineering. IEE Proceedings - D, Control Theory Applications. 138:1991;256-266.