Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Nonlinear and direction-dependent dynamic process modelling using neural networks

Nonlinear and direction-dependent dynamic process modelling using neural networks

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IEE Proceedings - Control Theory and Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The paper discusses several methods of modelling complex nonlinear dynamics using neural networks. Particular reference is made to the problem of modelling direction-dependent relationships. A typical example of this would be top product composition control in a distillation column, where it is easier (i.e. faster) to make the product less pure than it is to make it more pure by an equivalent amount. Recurrent neural networks are identified as a potential method of modelling this type of relationship. The particular architecture chosen for this example is referred to as ‘semirecurrent’, since only past values of the predictions of the network are fed back to the input layer. This architecture is successfully used to model direction-dependent relationships in both simulated and actual industrial process data.

References

    1. 1)
      • C. Peel , M.J. Willis , M.T. Tham . A fast procedure for the training of neural networks. J. Process Control , 4 , 205 - 211
    2. 2)
      • K.J. Hunt , D. Sbarbaro . Neural networks for non-linear internal model control. IEE Proc. D , 5
    3. 3)
      • J. Thibault , V.V. Breusegem , A. Cheruy . On-line prediction of fermentation variables using neural networks. Biotechnol. Bioeng. , 1041 - 1048
    4. 4)
      • D.E. Rumelhart , G.E. Hinton , R.J. Williams . Learning representations by back-propagation errors. Nature , 533 - 536
    5. 5)
      • M.J. Willis , C. Di Massimo , G.A. Montague , M.T. Tham , A.J. Morris . Artificial neural networks in process engineering. IEE Proc. D , 3 , 256 - 266
    6. 6)
      • R.G. Brown . (1963) Smoothing, forecasting and prediction of discrete time series.
    7. 7)
      • Sorsa, T., Koivo, H.N.: `Application of neural networks in the detection of breaks in a papermachine', IFAC symposium on On-line fault detection and supervisionin the process industries, 22–24 April 1992, Newark, Delaware, p. 162–167.
    8. 8)
      • Koshijima, I., Niida, K.: `Neural network approach to fault detection under steady state operation', IFAC symposium on On-line fault detection and supervisionin the process industries, 22–24 April 1992, Newark, Delaware, p. 174–179.
    9. 9)
      • R.D. Tyagi , Y.G. Du , T.R. Sreekrishnan , J.P. Villeneuve . Neural model for operational control of activated sludge processes. Process. Biochemistry , 4 , 259 - 267
    10. 10)
      • S.-Z. Qin , H.-T. Su , T.J. McAvoy . Comparison of four neural net learning methods for dynamic system identification. IEEE Trans. on Neural Networks , 1
    11. 11)
      • G. Cybenko . Approximation by superpositions of sigmoidal function. Math. Control Signal Syst. , 303 - 314
    12. 12)
      • S.A. Billings , H.B. Jamaluddin , S. Chen . Properties of neural networks with applications to modelling non-lineardynamical systems. Int. J. Control , 1 , 193 - 224
    13. 13)
      • C.A. Terzuolo , T.A. McKeen , R.E. Poppele , N.P. Rosenthal , C.A. Terzuolo . (1969) Impulse trains, coding and decoding, Systems analysis to neurophysiological problems.
    14. 14)
      • A.V. Holden . (1976) Models of the stochastic activity of neurons.
    15. 15)
      • Glassey, J., Montague, G.A., Willis, M.J., Morris, A.J.: `Considerations in process applications of artificial neural networks', Colloquium on Neural networks and fuzzy logic in measurementand control, March 1993, John Moores UniversityLiverpool.
    16. 16)
      • R.J. Williams , D. Zipser . A learning algorithm for continually running, fully recurrent neuralnetworks. Neural Computat. , 1270 - 1289
http://iet.metastore.ingenta.com/content/journals/10.1049/ip-cta_19960061
Loading

Related content

content/journals/10.1049/ip-cta_19960061
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address