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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Multi-objective Evolutionary Algorithm for Temporal Linguistic Rule Extraction
verfasst von : Gary G. Yen
Erschienen in: Multi-Objective Machine Learning
Verlag: Springer Berlin Heidelberg
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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.