Abstract
The article deals with automatic acquisition of control algorithms from process or device operation data. Our objective is to present a methodology for elimination of the mathematical modelling and programming stages in the control system development. In the presented approach these stages are replaced by a training stage followed by generation of decision rules equivalent to a Boolean network. The rules are produced from logged operation data obtained from experienced operators. The rules are subsequently converted into control program source code. The adapted training methodology has been developed within the framework of the theory of rough sets [1] and implemented as a commercial system for data analysis and rules extraction system by REDUCT Systems Inc., Regina, Canada. The presentation is illustrated with a comprehensive example demonstrating the generation of the control algorithms from simulated data representing movements of the robot arm.
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References
Pawlak, Z. (1991) Rough Sets: Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, Dordrecht.
Grzymala-Busse, J. (1988) “Knowledge Acquisition Under Uncertainty–a Rough Set Approach”, Journal of Intelligent and Robotic Systems, 1, pp. 3–16.
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Ziarko, W., and Katzberg, J. (1989) “Control Algorithms Acquisition, Analysis and Reduction: Machine Learning Approach” in Knowledge-Based System Diagnosis, Supervision and Control, Plenum Press, pp. 167–178.
Mrozek, A. (1989) “Rough Set Dependency Analysis Among Attributes in Computer Implementation of Expert Inference Models,” International Journal of Man-Machine Studies. 30, pp. 457–473.
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© 1992 Springer Science+Business Media Dordrecht
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Ziarko, W.P. (1992). Acquisition of Control Algorithms from Operation Data. In: Słowiński, R. (eds) Intelligent Decision Support. Theory and Decision Library, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-7975-9_5
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DOI: https://doi.org/10.1007/978-94-015-7975-9_5
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4194-4
Online ISBN: 978-94-015-7975-9
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