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Computers & chemical engineering, v.19 no.10, 1995년, pp.1077 - 1088
Macmurray, J.C. (Department of Chemical Engineering, The University of Texas, Austin, TX 78712, U.S.A.) , Himmelblau, D.M. (Department of Chemical Engineering, The University of Texas, Austin, TX 78712, U.S.A.)
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AbstractArtificial neural networks, because they are nets of basis functions, can provide good empirical models of complex nonlinear processes that are useful for many purposes including process control. The modelling of a packed distillation column described here provides an interesting example of ...
Ind. Engng chem. Res. Bequette 30 1391 1991 10.1021/ie00055a001 Nonlinear control of chemical processes: a review
Bhat 1342 1989 Use of neural networks for dynamic modeling and control of chemical process systems
Computers chem. Engng Bhat 14 573 1990 10.1016/0098-1354(90)87028-N Use of neuml nets for dynamic modeling and control of chemical process systems
Blum 1992 Model predictive control using artificial neural networks
Blum 1992 Practical issues in applying artificial neural networks for identification in model based predictive control
Int. J. Syst. Sci. Chen 21 2513 1990 10.1080/00207729008910567 Non-linear system identification using radial basis functions
Computers chem. Engng Downs 13 245 1993 10.1016/0098-1354(93)80018-I A plant-wide industrial process control problem
Computers chem. Engng Hoskins 12 881 1988 10.1016/0098-1354(88)87015-7 Artificial neural network models of knowledge representation in chemical engineering
Automatka Hunt 28 1083 1992 10.1016/0005-1098(92)90053-I Neural networks for control systems-A survey
Karjala 1992 10.1109/IJCNN.1992.226873 Data rectification using recurrent (Elman) neural networks
Computers chem. Engng Kramer 14 1323 1990 10.1016/0098-1354(90)80015-4 Diagnosis using back-propagation neural networks-analysis and criticism
Lambert 1 373 1991 Application of feedforward and recurrent neural networks to chemical plant predictive modeling
Lee 1991 A new control scheme combining neural network based feedforward control with model predictive control
Computers chem. Engng Leonard 16 819 1992 10.1016/0098-1354(92)80035-8 Neural network architecture that computes its own reliability
IEEE Trans Neural Networks Leonard 3 624 1992 10.1109/72.143377 Using radial basis functions to approximate a function and its error bounds
Int. J. Control. Leontardis 41 303 1985 10.1080/0020718508961129 Input-output parametric model for nonlinear system part 1 and 2
Ljung 1987 System Identification: Theory for the User
Miller 1990 Neural Networks for Control
Montague 231 1992 Predictive control of distillation columns using dynamic neural networks
Montague 1990 Dynamic modeling of industrial processes with artificial neural networks
Morari 1986 Chemical Process Control-CPCIII
Morari 1989 Robust Process Control
IEEE Trans. Neural Network Narendra 1 4 1990 10.1109/72.80202 Identification and control of dynamical systems using neural networks
Patwardhan 1991 Modeling and control of a packed distillation column
Chem. Engng Commun. Patwardhan 87 123 1990 10.1080/00986449008940687 Nonlinear model predictive control
Pottmann 309 1992 A non-linear predictive control strategy based on radial basis function networks
Powell 1987 Radial Basis Functions for Multivariable Interpolation: A Review
Ind. Engng Chem. Res. Psichogios 30 2564 1991 10.1021/ie00060a009 Direct and indirect model based control using artificial neural networks
Int. J. Contr. Saint-Donat 54 1453 1991 10.1080/00207179108934221 Neural net based model predictive control
Neural Comput. Scott 4 746 1992 10.1162/neco.1992.4.5.746 Refining PID controllers using neural networks
Su 2314 1991 Identification of chemical processes using recurrent networks
Ind. Engng Chem. Res. Su 31 1338 1992 10.1021/ie00005a014 Long-term prediction of chemical processes using recurrent neural networks: a parallel training approach
Computers chem. Engng Ungar 14 572 1990 10.1016/0098-1354(90)87027-M Adaptive networks for fault diagnosis and process control
Proc. IEE, Pt. D Willis 138 256 1993 10.1049/ip-d.1991.0036 On neural networks in chemical process control
Automatka Willis 28 1181 1992 10.1016/0005-1098(92)90059-O Artificial neural networks in process estimation and control
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