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Artificial Intelligence in Real-Time Control 1992
Selected Papers from theIFAC/IFIP/IMACS Symposium, Delft, Netherlands, 16-18 June 1992
- 1st Edition - November 23, 1993
- Authors: M.G. Rodd, H.B. Verbruggen
- Language: English
- eBook ISBN:9 7 8 - 1 - 4 8 3 2 - 9 9 0 2 - 0
The symposium had two main aims, to investigate the state-of-the-art in the application of artificial intelligence techniques in real-time control, and to bring together control… Read more
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Request a sales quoteThe symposium had two main aims, to investigate the state-of-the-art in the application of artificial intelligence techniques in real-time control, and to bring together control system specialists, artificial intelligence specialists and end-users. Many professional engineers working in industry feel that the gap between theory and practice in applying control and systems theory is widening, despite efforts to develop control algorithms. Papers presented at the meeting ranged from the theoretical aspects to the practical applications of artificial intelligence in real-time control. Themes were: the methodology of artificial intelligence techniques in control engineering; the application of artificial intelligence techniques in different areas of control; and hardware and software requirements. This symposium showed that there exist alternative possibilities for control based on artificial intelligence techniques.
For theorists and engineers with an interest in developing applications for AI in real-time control in industry.
Section headings and selected papers: Plenary Papers. Knowledge based control: selecting the right tool for the job (R. Leitch). The Methodology of Artificial Intelligence Techniques in Control Systems. Neural Net Control. Neural networks applied to optimal flight control (T. McKelvey). The influence of training data selection on performance of neural networks for control of non-linear systems (A.B. Bendtsen, N. Jensen). Knowledge-Based Control. Induction of control rules from human skill (K.J. Hunt, Y.M. Han). Dimensions of learning in a real-time knowledge-based control system (N.V. Findler). Fuzzy Control. Fuzzy inference in rule-based real-time control (R. Jager et al.). Monitoring and Fault Diagnosis. Supervisory control of mode-switch processes: application to a flexible beam (R.A. Hilhorst et al.). Supervision and control of an exothermic batch process (R. Perne). Genetic Algorithms and Learning. An adaptive system for process control using genetic algorithms (C.L. Karr). Qualitative Reasoning. On representations for continuous dynamic systems (E.A. Woods). The Application of Artificial Intelligence Techniques in Different Areas of Control. Process Control. Real-time supervisory control for industrial processes (D.A. Linkens, M.F. Abbod). Biotechnology. Pattern recognition for bioprocess control (B. Sonnleitner, G. Locher). Hardware and Software Requirements. Temporal Reasoning. A temporal blackboard structure for process control (F. Barber et al.). New Paradigms for Real-Time Control. Reinforcement learning and recruitment mechanism for adaptive distributed control (H. Bersini). Real-Time Environments for Intelligent Control. DICE: a real-time toolbox (A.J. Krijgsman, R. Jager). Development of Real-Time AI Systems. An execution environment for real-time model-based supervisory control and diagnostic systems (Z. Papp et al.). Author index. Keyword index.
Numerous illus., 4 half-tones.
- Language: English
- Edition: 1
- Published: November 23, 1993
- Imprint: Pergamon
- eBook ISBN: 9781483299020
MR
M.G. Rodd
Affiliations and expertise
University of Wales, Swansea, UKHV
H.B. Verbruggen
Affiliations and expertise
The NetherlandsRead Artificial Intelligence in Real-Time Control 1992 on ScienceDirect