Process control and automation
- Introduction to process control and automation
- Development of process automation
- Fibre process automation
- Chemical recovery as a control object
- Advances in paper machine automation
- Paper machine as a dynamical system
- Tasks in paper machine control and management
- Control of stock flow concentration and quality
- Machine direction control
- Cross-directional control – The static optimisation
- Cross-directional control – Dynamics
- Cross-directional control – further aspects
- Controlling functional paper properties
- Managing grade chances in the paper machine
- Managing disturbances caused by broke and recovered solids
- Millwide systems
- Modelling and control methods
Recurrent neural networks A recurrent neural network (RNN) exhibits temporal dynamic behaviour. Its connections between nodes form a directed graph capable of representing a temporal sequence. The Elman 1 and Jordan 2 networks are simple recurrent neural networks. The next presentation considers Elman networks. More information can be found in the existing literature. Elman networks
Authors & references
Authors:
Professor Emeritus Kauko Leiviskä, University of Oulu
References:
- Elman, J.L. 1990. Finding Structure in Time. Cognitive Science 14 (2):179–211.
- Jordan, M. I. 1997. Serial Order: A Parallel Distributed Processing Approach. Advances in Psychology. Neural-Network Models of Cognition. 121: 471–495.
- Pham, D.T. and Karaboga, D. 1999. Training Elman and Jordan networks for system identification using genetic algorithms. Artificial Intelligence in Engineering 13:107–117.
- Wang, D.L., Liu, X.M., Ahalt, S.C 1996. On temporal generalization of simple recurrent networks. Neural Networks 9(7):1099-1118.
- Abdelhameed, M. M. and Tolbah, F.A. 2002. A recurrent neural network-based sequential controller for manufacturing automated systems. Mechatronics 12: 617–633.
- Leiviskä, K. 2009. Elman Network in Kappa Number Prediction. -Proceedings, ICONS 2009. The 2nd IFAC Conference, Intelligent Control Systems. Istanbul, Turkey, 6p.
- Koprinkova-Hristova, P., Hadjiski, M. and Doukovska, L., and Beloreshki, S. 2011. Recurrent Neural Networks for Predictive Maintenance of Mill Fan Systems. Intl journal of electronics and telecommunications 57(3):401–406.
- Köker, R. 2005a. Reliability-based approach to the inverse kinematics solution of robots using Elman’s networks. Engineering Applications of Artificial Intelligence 18:685–693.
- Köker, R. 2006 b. Design and performance of an intelligent predictive controller for a six-degree-of-freedom robot using the Elman network. Information Sciences 176:1781–1799.
- Patnaik, P.R. 2003. An integrated hybrid neural system for noise filtering, simulation and control of a fed-batch recombinant fermentation. Biochemical Engineering Journal 15:165–175.
- He, H.-T. and Tian, X. 2007. An improved Elman Network and its application in flatness prediction modeling Proceedings of the Second International Conference on Innovative Computing, Information and Control, pp. 552–558
- Villatel, K., Smirnova, E., Mary, J. and Preux, P. 2018. Recurrent Neural Networks for Long and Short-Term Sequential Recommendation. CoRR, abs/1807.09142.
- Seker, S., Ayaz, E. and Türkcan, E. 2003. Elman’s recurrent neural network applications to condition monitoring in nuclear power plant and rotating machinery. Engineering Applications of Artificial Intelligence 16(7–8):647–656.
Videos
Exercises
This page has been updated 15.11.2020