An integrated neural-network and expert-system approach to the supervision of reactor operating states in polyethylene terephthalate production

Control Engineering Practice

Volumn 6, Issue 5, 1998, Pages 581-591

  Zhang, Jie ^a,c   Yang, Qiugui ^a   Zhang, Suzhen ^a   Howell, John ^b  

^a EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b UNIVERSITY OF GLASGOW   (United Kingdom)

^c IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

artificial neural networks; expert systems; non linear optimisation; pattern recognition; Quasi Newton; self scaling variable metrics

Indexed keywords

QUASI-NEWTON METHOD; SELF-SCALING VARIABLE METRICS (SSVM);

COMPUTER SIMULATION; DISTRIBUTED PARAMETER CONTROL SYSTEMS; ERROR ANALYSIS; EXPERT SYSTEMS; INTELLIGENT CONTROL; LEARNING ALGORITHMS; NEURAL NETWORKS; NONLINEAR PROGRAMMING; PATTERN RECOGNITION; POLYETHYLENE TEREPHTHALATES; SCADA SYSTEMS;

PRODUCTION CONTROL;

EID: 0032076393     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0967-0661(98)00033-1     Document Type: Article

References (17)

1. Becraft, W.R., Lee, P.L., 1993. An integrated neural network/expert system approach for fault diagnosis. Comput. Chem. Engng. 17(10), 1001-1014.
2. Beigi, H.S.M, Li, C.J., 1993. Learning algorithms for neural networks based on Quasi-Newton methods with self-scaling. J. of Dynamic Systems, Measurement and Control 115, 38-43.
3. Burr, D.J., 1988. Experiments on neural net recognition of spoken and written text. IEEE Trans. ASSP. 36, 1162-1168.
4. Jau, J.Y., Fainman, Y., Lee, S.H., 1989. Comparison of artificial neural networks with pattern recognition and image processing. Applied Optics 28, 1302-1309.
5. Lucas, A.E., Kittler, J., 1989. A comparative study of the Kohonen and multiedit neural networks. IEE Publication, No. 313, London, 1989.
6. Minsky, M., 1991. Logical versus analogical or symbolic versus connectionist or neat versus scruffy. AI Magazine 12(2), 34-51.
7. Oren, S.S., Spedicato, 1976. Optimal condition of self scaling variable metric algorithms. Mathematical Programming 10, 70-90.
8. Pao, Y.H., 1989. Adaptive pattern recognition and neural networks. Addison-Wesley, New York.
9. Rich, E., 1990. Expert systems and neural networks can work together. IEEE Expert 5(5), 5-9.
10. Robitaille, B., Marcos, B., Veillette, M., Payre, G., 1996. Modified Quasi-Newton methods for training neural networks. Computers Chem. Engng 20(9), 1133-1140.
11. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning internal representations by error propagation. In Rumelhart, D.E., McClelland, J.L. (eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, MIT Press, Cambridge.
12. Schalkoff, R., 1992. Pattern Recognition. Statistical, Structural and Neural Approaches. John Wiley & Sons, Inc.
13. Zhang, J., Zhang, S.Z., 1993. A semi-automatic knowledge acquisition tool for expert system. Proc. of 4th China Control and Decision Conference. Huangshan, P.R.China, May 1993, 530-533.
14. Zhang, J., Zhang, S.Z., Jiang, W. S., 1995. An improved decision-table-based method to verify knowledge base of expert systems. Proc. of Pacific-Asian Conf. on Expert Systems, Huangshan, P.R. China, May 1995, 218-222.
15. Zhang, J., Zhang, S.Z., Jiang, W.S., 1996a. Knowledge verification for expert systems: I. Some Issues. Journal of East China University of Sci. and Tech. 22, 474-479.
16. Zhang, J., Zhang, S.Z., Jiang, W.S., 1996b. Knowledge verification for expert systems: II. Decision Tables-based Knowledge Base Verifier KBV. Journal of East China University of Sci. and Tech. 22, 584-588
17. Zhang, J., Zhang, S.Z., Howell, J., 1997. Knowledge based gross error detection and data reconciliation to polyethylene terephthalate process. IFAC Symposium Safeprocess '97, 1, 234-239, Hull.