References
- Åkesson, B. M. and Toivonen, H. T. (2006). A neural network model predictive controller, Journal of Process Control 16(3): 937-946.
- Alexandridis, A. and Sarimveis, H. (2005). Nonlinear adaptive model predictive control based on self-correcting neural network models, AIChE Journal 51(9): 2495-2506.
- Bazaraa, M. S., Sherali, J. and Shetty, K. (1993). Nonlinear Programming: Theory and Algorithms, John Wiley & Sons, New York, NY.
- da Cruz Meleiro, L. A., José, F., Zuben, V. and Filho, R. M. (2009). Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process, Engineering Applications of Artificial Intelligence 22(2): 201-215.
- Doyle, F. J., Ogunnaike, B. A. and Pearson, R. K. (1995). Nonlinear model-based control using second-order Volterra models, Automatica 31(5): 697-714.
- El Ghoumari, M. Y. and Tantau, H. J. (2005). Non-linear constrained MPC: Real-time implementation of greenhouse air temperature control, Computers and Electronics in Agriculture 49(3): 345-356.
- Greco, C., Menga, G., Mosca, E. and Zappa, G. (1984). Performance improvement of self tuning controllers by multistep horizons: The MUSMAR approach, Automatica 20(5): 681-700.
- Haykin, S. (1999). Neural Networks. A Comprehensive Foundation, 2nd Edition, Prentice-Hall, Englewood Cliffs, NJ.
- Henson, M. A. (1998). Nonlinear model predictive control: Current status and future directions, Computers and Chemical Engineering 23(2): 187-202.
- Ławryńczuk, M. (2007). A family of model predictive control algorithms with artificial neural networks, International Journal of Applied Mathematics and Computer Science 17(2): 217-232, DOI: 10.2478/v10006-007-0020-5.
- Ławryńczuk, M. (2008). Suboptimal nonlinear predictive control with neural multi-models, in L. Rutkowski, R. Tadeusiewicz, L. A. Zadeh and J. Zurada (Eds), The 9th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2008, Zakopane, Poland (Computational Intelligence: Methods and Applications), Exit, Warsaw, pp. 45-56.
- Ławryńczuk, M. (2009a). Computationally efficient nonlinear predictive control based on RBF neural multi-models, in M. Kolehmainen, P. Toivanen and B. Beliczyński (Eds), The Ninth International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009, Kuopio, Finland, Lecture Notes in Computer Science, Vol. 5495, Springer, Heidelberg, pp. 89-98.
- Ławryńczuk, M. (2009b). Efficient nonlinear predictive control based on structured neural models, International Journal of Applied Mathematics and Computer Science 19(2): 233-246, DOI: 10.2478/v10006-009-0019-1.
- LeCun, Y., Denker, J. and Solla, S. (1990). Optimal brain damage, in D. Touretzky (Ed.), Advances of NIPS2, Morgan Kaufmann, San Mateo, CA, pp. 598-605.
- Liu, D., Shah, S. L. and Fisher, D. G. (1999). Multiple prediction models for long range predictive control, Proceedings of the IFAC World Congress, Beijing, China, (on CD-ROM).
- Lu, C. H. and Tsai, C. C. (2008). Adaptive predictive control with recurrent neural network for industrial processes: An application to temperature control of a variable-frequency oil-cooling machine, IEEE Transactions on Industrial Electronics 55(3): 1366-1375.
- Luyben, W. L. (1990). Process Modelling, Simulation and Control for Chemical Engineers, McGraw Hill, New York, NY.
- Maciejowski, J. M. (2002). Predictive Control with Constraints, Prentice Hall, Harlow.
- Morari, M. and Lee, J. (1999). Model predictive control: Past, present and future, Computers and Chemical Engineering 23(4): 667-682.
- Narendra, K. S. and Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks, IEEE Transactions on Neural Networks 1(1): 4-26.
- Nørgaard, M., Ravn, O., Poulsen, N. K. and Hansen, L. K. (2000). Neural Networks for Modelling and Control of Dynamic Systems, Springer, London.
- Parisini, T., Sanguineti, M. and Zoppoli, R. (1998). Nonlinear stabilization by receding-horizon neural regulators, International Journal of Control 70(3): 341-362.
- Pearson, R. K. (2003). Selecting nonlinear model structures for computer control, Journal of Process Control 13(1): 1-26.
- Peng, H., Yang, Z. J., Gui, W., Wu, M., Shioya, H. and Nakano, K. (2007). Nonlinear system modeling and robust predictive control based on RBF-ARX model, Engineering Applications of Artificial Intelligence 20(1): 1-9.
- Qin, S. J. and Badgwell, T. (2003). A survey of industrial model predictive control technology, Control Engineering Practice 11(7): 733-764.
- Qin, S. Z., Su, H. T. and McAvoy, T. J. (1992). Comparison of four neural net learning methods for dynamic system identification, IEEE Transactions on Neural Networks 3(1): 122-130.
- Rossiter, J. A. and Kouvaritakis, B. (2001). Modelling and implicit modelling for predictive control, International Journal of Control 74(11): 1085-1095.
- Su, H. T. and McAvoy, T. J. (1992). Long-term predictions of chemical processes using recurrent neural networks: A parallel training approach, Industrial and Engineering Chemistry Research 31(5): 1338-1352.
- Tatjewski, P. (2007). Advanced Control of Industrial Processes, Structures and Algorithms, Springer, London.
- Tatjewski, P. and Ławryńczuk, M. (2006). Soft computing in model-based predictive control, International Journal of Applied Mathematics and Computer Science 16(1): 101-120.
- Temeng, K. O., Schnelle, P. D. and McAvoy, T. J. (1995). Model predictive control of an industrial packed bed reactor using neural networks, Journal of Process Control 5(1): 19-27.