Knowledge-based systems for industrial control
Author(s)
Bibliographic Information
Knowledge-based systems for industrial control
（IEE control engineering series, v. 44）
P. Peregrinus Ltd. on behalf of the Institution of Electrical Engineers, c1990
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
Part 1: Background for knowledge-based control
Chapter 1: Holistic approaches in knowledge-based process control
Chapter 2: Introduction to knowledge-based systems for process control
Chapter 3: Basic theory and algorithms for fuzzy sets and logic
Chapter 4: Knowledge engineering and process control
Part 2: Artificial intelligence issues
Chapter 5: Cognitive models from subcognitive skills
Chapter 6: A review of the approaches to the qualitative modelling of complex systems
Chapter 7: Solving process engineering problems using artificial neural networks
Chapter 8: Parallel processing architecture for real-time control
Part 3: Applications of knowledge expertise
Chapter 9: Overview of artificial intelligence tools
Chapter 10: Application of fuzzy logic to control and estimation problems
Chapter 11: Real-time knowledge-based systems in fermentation supervisory control
Chapter 12: Machine-learned rule-based control
Chapter 13: Expert systems for self-tuning control
Chapter 14: Case studies in condition monitoring
Chapter 15: COGSYS - the real-time expert system builder
Chapter 16: Application of COGSYS to a small gas-processing plant
Part 4: Deductive control issues
Chapter 17: Expert system issues for multivariable control
Chapter 18: Design of LQG and H multivariable robust controllers for process control applications
by "Nielsen BookData"
