Empirical Learning Through Neural Networks: The Wave-Net Solution

Advances in Chemical Engineering

Volumn 22, Issue C, 1995, Pages 437-484

  Koulouris, Alexandros ^a   Bakshi, Bhavik R ^a,b   Stephanopoulos, George ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0042482544     PISSN: 00652377     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0065-2377(08)60264-1     Document Type: Chapter

References (42)

1. Bakshi, B., Stephanopoulos, G., AIChE J., 39, 1993, 57.
2. Bakshi, B., Koulouris, A., Stepanopoulos, G., Learning at multiple resolutions: Wavelets as basis functions in artificial neural networks and inductive decision trees. Motard, R.L., Joseph, B., (eds.) Wavelet Applications in Chemical Engineering, 1994, Kluwer Academic Publishers, Dordrecht/Norwell, MA, 139.
3. Barron, A.R., Approximation and estimation bounds for artificial neural networks. Mach. Learn., 14, 1994, 115.
4. Barron, A.R., Barron, R.L., Statistical learning networks: A unifying view. Symposium on the Interface: Statistics and Computing Science., 1988, Reston, VA, 192.
5. Barto, A.G., Connectionist learning for control. Miller, W.T., Sutton, R.S., Werbos, P.J., (eds.) Neural Networks for Control., 1991, MIT Press, Cambridge, MA.
6. Bhat, N.V., McAvoy, T.J., Use of neural nets for dynamic modeling and control of chemical process systems. Comput. Chem. Eng., 14, 1990, 573.
7. Bhat, N.V., McAvoy, T.J., Determining the structure for neural models by network stripping. Comput. Chem. Eng., 16, 1992, 271.
8. Cohen, A., Biorthogonal wavelets. Chui, C.K., (eds.) Wavelets–A Tutorial in Theory and Applications, 1992, Academic Press, San Diego, CA, 123.
9. Daubechies, I., Ten Lectures on Wavelets., 1992, SIAM Philadelphia.
10. Feauveau, J.C., Nonorthogonal multiresolution analysis using wavelets. Chui, C.K., (eds.) Wavelets–A Tutorial in Theory and Applications, 1992, Academic Press, San Diego, CA, 153.
11. Girosi, F., Rates of Convergence of Approximation by Translates and Dilates, 1993, AI Lab Memo, Massachusetts Institute of Technology, Cambridge, MA.
12. Girosi, F., Anzellotti, G., Rates of convergence for radial basis functions and neural networks. Mammone, R.J., (eds.) Artificial Neural Networks with Applications in Speech and Vision, 1993, Chapman & Hall, London, 97.
13. Hartman, E., Keeler, K., Kowalski, J.K., Layered Neural Networks with Gaussian hidden units as universal approximators. Neural Comput., 2, 1990, 210.
14. Hernandez, E., Arkun, Y., A study of the control relevant properties of backpropagation neural net models of nonlinear dynamical systems. Comput. Chem. Eng., 16, 1992, 227.
15. Hornik, K., Stinchcombe, M., White, H., Multi-layer feedforward networks are universal approximators. Neural Networks, 2, 1989, 359.
16. Hoskins, J.C., Himmelblau, D.M., Artificial neural network models of knowledge representation in chemical engineering. Comput. Chem. Eng., 12, 1988, 881.
17. Kearns, M., Vazirani, U., An Introduction to Computational Learning Theory., 1994, MIT Press, Cambridge, MA.
18. Kon, M., and Raphael, L., “Convergence Rates of Wavelet Expansions,” preprint 1993.
19. Koulouris, A., and Stepanopoulos, G., On-line empirical learning of process dynamics with Wave-Nets, submitted to Comput. Chem. Eng. (1995).
20. Kovačević, J., Vetterli, M., Nonseparable multiresolutional perfect reconstruction banks and wavelet bases for Rn. IEEE Trans. Inf. Theory, 38, 1992, 533.
21. Kramer, M. A., Thompson, M. L. and Bhagat, P. M., Embedding theoretical models in neural networks. Proc. Am. Control Conf. 475(1992).
22. Kreinovich, V.Y., Arbitrary nonlinearity is sufficient to represent all functions by neural networks: A theorem. Neural Networks, 4, 1991, 381.
23. Lee, M., Park, S., A new scheme combining neural feedforward control with model predictive control. AIChE J., 38, 1992, 193.
24. Leonard, J.A., Kramer, M.A., Radial basis function networks for classifying process faults. IEEE Control Syst. 11 (1991), 31–38.
25. Ljung, L., System Identification: Theory for the User., 1987, Prentice-Hall Englewood Cliffs, NJ.
26. Mallat, S.G., A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell., PAMI-11, 1989, 674.
27. Mavrovouniotis, M.L., Chang, S., Hierarchical Neural Networks. Comput. Chem. Eng., 16, 1992, 347.
28. Moody, J., Darken, C.J., Fast learning in networks of locally-tuned processing units. Neural Comput., 1, 1989, 281.
29. Murty, K.G., Linear Programming., 1983, Wiley, New York.
30. Narendra, K.S., Parthasarathy, K., Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks, 1, 1990.
31. Pati, Y. C., Wavelets and time-frequency methods in linear systems and neural networks. Ph. D. Thesis, University of Maryland, College Park (1992).
32. Platt, J., A resource-allocating network for function interpolation. Neural Comput., 3, 1991, 213.
33. Poggio, T., Girosi, F., “A Theory of Networks for Approximation and Learning,” AI Lab. Memo. No. 1140, 1989, Massachusetts Institute of Technology, Cambridge, MA.
34. Psichogios, D.C., Ungar, L.H., Direct and indirect model based control using artificial neural networks. Ind. Eng. Chem. Res., 30, 1991, 2564.
35. Psichogios, D.C., Ungar, L.H., A hybrid neural network-first principles approach to process modeling. AIChE J., 38, 1992, 1499.
36. Rengaswamy, R., and Venkatasubramaniam, V., Extraction of qualitative trends from noisy process data using neural networks. AIChE Annu. Meet. Los Angeles (1991).
37. Rumelhart, D.E., McClelland, J.L., the PDP Research Group. Parallel Distributing Processing., 1986, MIT Press, Cambridge, MA.
38. Strang, G., Wavelets and dilation equations: A brief introduction. SIAM Rev., 31, 1989, 614.
39. Ungar, L.H., Powell, B.A., Kamens, S.N., Adaptive Networks for fault diagnosis and process control. Comput. Chem. Eng., 14, 1990, 561.
40. Vapnik, V., Estimation of Dependences Based on Empirical Data., 1982, Springer-Verlag, Berlin.
41. Ydstie, B.E., Forecasting and control using adaptive connectionist networks. Comput. Chem. Eng., 14, 1990, 583.
42. Zhang, Q., Benveniste, A., Wavelet networks. IEEE Trans. Neural Networks, 3, 1992, 889.