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Knowledge-based Systems for Industrial Control (Control, Robotics and Sensors) ePub download

by J. McGhee,M.J. Grimble,P. Mowforth

  • Author: J. McGhee,M.J. Grimble,P. Mowforth
  • ISBN: 0863412211
  • ISBN13: 978-0863412219
  • ePub: 1520 kb | FB2: 1253 kb
  • Language: English
  • Category: Hardware & DIY
  • Publisher: The Institution of Engineering and Technology (June 30, 1990)
  • Pages: 356
  • Rating: 4.3/5
  • Votes: 710
  • Format: doc rtf mobi lrf
Knowledge-based Systems for Industrial Control (Control, Robotics and Sensors) ePub download

Background for knowledge-based control: Holistic approaches in knowledge-based process control; introduction to knowledge-based systems for process control; basic theory and algorithms for fuzzy sets and logic; knowledge engineering and process control.

Background for knowledge-based control: Holistic approaches in knowledge-based process control; introduction to knowledge-based systems for process control; basic theory and algorithms for fuzzy sets and logic; knowledge engineering and process control.

McGhee, J; Grimble, Michael J; Mowforth, P. (Peter), 1953-; Institution of Electrical Engineers. Automatic control, Process control, Expert systems (Computer science). P. Peregrinus Ltd. on behalf of the Institution of Electrical Engineers. inlibrary; printdisabled; ; china.

Mcghee and Michael J. Grimble and Peter Mowforth}, year {1990} }.

Recognising the importance of this emerging area, the Institution of Electrical Engineers organised a Vacation School on the subject, for engineers from industry and academia, at the University of Strathclyde in September 1990.

Publication Year: 1990. Background for knowledge-based control: Holistic approaches in knowledge-based process control; introduction to knowledge-based systems for process control; basic theory and algorithms for fuzzy sets and logic; knowledge engineering and process control.

An RFID-based distributed control system for flexible manufacturing system.

This paper proposes the fundamentals of control systems in robotics and various types of control systems in. robotics. Each type of control system has its pros and cons which have been discussed in this paper. An RFID-based distributed control system for flexible manufacturing system.

CrossRefGoogle Scholar. Amethyst: An Expert System for the Diagnosis of Rotating Machinery’. Proceedings of Comadem 90 Conference on Condition Monitoring and Diagnostic Engineering Management, Brunei University, England, p 287–292,16th–18th July, 1990. On-Line Diagnostic Expert System For Gas Turbines’. Tooldiag’, Toulouse, France.

IEEE Robotics & Automation Society Young Roboticist Award Brian Carr (School pupil), St Patricks High School, Coatbridge, Scotland. McGee, Grimble and Mowforth. Peregrinus Limited. Awarded £25 book token. NatWest Bank Prize for Technology Transfer Olaf Beck, Prof. Rodney Brookes & Colin Angle, Massachusetts Institute of Technology MIT, AI Lab, USA Awarded with a Caithness Crystal bowl and £200 from NatWest Bank. Turing Institute Best School Prize' XYBOT Inverkeithing School, Class 7S, Scotland. University of Strathclyde Archives.

Lewis Volume 51 Advanced robotics and intelligent machines .

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Expert and knowledge-based systems have great potential for industrial control systems, particularly in the process industries. Recognising the importance of this emerging area, the Institution of Electrical Engineers organised a Vacation School on the subject, for engineers from industry and academia, at the University of Strathclyde in September 1990.

The course and this resulting text cover four main issues: the background of knowledge-based control, artificial intelligence, applications of knowledge expertise, and deductive control.

The background material presents knowledge based control from the perspective of Systems Engineering and Information Technology. When combined with an introduction to both artificial intelligence and fuzzy logic, a firm foundation is laid for consolidation of the later material of the book.

The importance of fuzzy control is considered and the use of expert systems in self-tuning control is discussed. The use of real-time knowledged based systems in Fermentation supervisory control is described and the impact of neural networks in process modelling is also considered. The development of COGSYS which is an environment for building expert systems and its applications is described, together with its application to a gas processing plant.

Case studies in condition monitoring are presented. The development of qualitative models for physical systems which is currently attracting considerable interest from the Al community is considered. The design of multivariable control systems and other aspects of process control are also covered.

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