Abstract
In this work we discuss the development of an expert system with approximate reasoning which resorts to a new methodology for attribute selection in knowledge-based systems. First, we make a survey of the purifying process and its problems, as well as those of conventional automatic control methods applied to industrial processes. Next, we establish a definition of the relevance concept for a given set of attributes, which includes the special case of non-relevant attributes or nought attributes. A new heuristic is here proposed in such a way that it finds out the more relevant attributes from those initially selected by the expert, reducing the cost of the formation & validation of decision rules and helping to clarify the underlying structure of a non well-structured domain as are waste-water treatment plants.
Partially supported by GRANT TIC-90 801/C02. CICyT. ESPAÑA
Partially supported by GRANT ROB-89 0479-C03-02. CICyT. ESPAÑA
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© 1992 Springer-Verlag Berlin Heidelberg
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Belanche, L., Sànchez, M., Cortés, U., Serra, P. (1992). A knowledge-based system for the diagnosis of waste-water treatment plants. In: Belli, F., Radermacher, F.J. (eds) Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. IEA/AIE 1992. Lecture Notes in Computer Science, vol 604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0024984
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DOI: https://doi.org/10.1007/BFb0024984
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