Abstract
Autonomous temporal linguistic rule extraction is an application of growing interest due to its relevance to both decision support systems and fuzzy controllers. In the presented work, rules are evaluated using three qualitative metrics based on their representation on the truth space diagram. Performance metrics are then treated as competing objectives and Multiple Objective Evolutionary Algorithm is used to search for an optimal set of non-dominated rules. Novel techniques for data pre-processing and rule set post-processing are developed that deal directly with the delays involved in dynamic systems. Data collected from a simulated hot and cold water mixer and a two-phase vertical column is used to validate the proposed procedure.
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Yen, G.G. (2006). Multi-objective Evolutionary Algorithm for Temporal Linguistic Rule Extraction. In: Jin, Y. (eds) Multi-Objective Machine Learning. Studies in Computational Intelligence, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33019-4_16
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DOI: https://doi.org/10.1007/3-540-33019-4_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30676-4
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