Abstract
The design and implementation of industrial control systems often relies on quantitative models. At times, however, we encounter problems for which such models do not exist or are difficult and expensive to obtain. In such cases it is often possible to consult human experts to create qualitative models. This approach is the cornerstone of the application of fuzzy logic to the synthesis of control systems [3]. Another approach consists in observing human operators of plants and processes and discovering rules governing their actions. The behavior of operators can often be specified by decision tables, defined as sets of decision rules coupled with rule selection mechanisms. Rough set theory [10, 11] can be used to generate such tables from protocols of control, containing the decisions of human operators [8].
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Mrózek, A., Płonka, L. (1998). Rough Sets in Industrial Applications. In: Polkowski, L., Skowron, A. (eds) Rough Sets in Knowledge Discovery 2. Studies in Fuzziness and Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1883-3_12
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DOI: https://doi.org/10.1007/978-3-7908-1883-3_12
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