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Knowledge-based Supervisory Process Control: Applying Fuzzy Sets to Blackboard Control Architecture
Dunedin, New Zealand November 20-November 23
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ANNES.1995.4994622nd New Zealand Two-Stream Internatio ...
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M. Fathi, K. Holte, C. Lueg, R. Scharnetzki, "Knowledge-based Supervisory Process Control: Applying Fuzzy Sets to Blackboard Control Architecture," Artificial Neural Networks and Expert Systems, New Zealand Conference, pp. 153, 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems (ANNES '95), 1995.
 
 
BibTex
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@article{ 10.1109/ANNES.1995.499462,
author = {M. Fathi and K. Holte and C. Lueg and R. Scharnetzki},
title = {Knowledge-based Supervisory Process Control: Applying Fuzzy Sets to Blackboard Control Architecture},
journal ={Artificial Neural Networks and Expert Systems, New Zealand Conference},
volume = {0},
year = {1995},
isbn = {0-8186-7174-2},
pages = {153},
doi = {http://doi.ieeecomputersociety.org/10.1109/ANNES.1995.499462},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Artificial Neural Networks and Expert Systems, New Zealand Conference
TI - Knowledge-based Supervisory Process Control: Applying Fuzzy Sets to Blackboard Control Architecture
SN - 0-8186-7174-2
SP
EP
A1 - M. Fathi,
A1 - K. Holte,
A1 - C. Lueg,
A1 - R. Scharnetzki,
PY - 1995
KW - Blackboard Architecture
KW - Process Control
KW - Blackboard System
KW - Monitoring Tasks
KW - Fuzzy Logik
KW - Expert Systems
VL - 0
JA - Artificial Neural Networks and Expert Systems, New Zealand Conference
ER -
 
M. Fathi, University of Dortmund
K. Holte, University of Dortmund
C. Lueg, University of Dortmund
R. Scharnetzki, University of Dortmund
The blackboard control architecture has proven to be qualified for tasks as complex as supervisory process control. The task structure framework permits a hierarchical decomposition of tasks into subtasks common in the domain of supervisory process control. Qualitative task descriptions based on fuzzy sets grant the integration of task structure framework while preserving the inherent flexibility of the blackboard control architecture. Fuzzy sets are further qualified to improve the basic control cycle. Linguistic variables allow the specification of control knowledge in a more natural way according to human knowledge.
Index Terms:
Blackboard Architecture, Process Control, Blackboard System, Monitoring Tasks, Fuzzy Logik, Expert Systems
Citation:
M. Fathi, K. Holte, C. Lueg, R. Scharnetzki, "Knowledge-based Supervisory Process Control: Applying Fuzzy Sets to Blackboard Control Architecture," annes, pp.153, 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems (ANNES '95), 1995
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