Abstract
For many real world applications a great deal of information is provided by human experts, who do not reason in terms of mathematics but instead describe the system verbally through vague or imprecise statements like, If The Temperature is Big then The Pressure is High. (3.1) Because so much human knowledge and expertise is given in terms of verbal rules, one of the sound engineering approaches is to try to integrate such linguistic information into the modelling process. A convenient and common approach of doing this is to use fuzzy logic concepts to cast the verbal knowledge into a conventional mathematics representation (model structure), which subsequently can be fine-tuned using input-output data.
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© 2007 Birkhäuser Verlag AG
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(2007). Clustering for Fuzzy Model Identification — Regression. In: Cluster Analysis for Data Mining and System Identification. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-7988-9_3
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DOI: https://doi.org/10.1007/978-3-7643-7988-9_3
Publisher Name: Birkhäuser Basel
Print ISBN: 978-3-7643-7987-2
Online ISBN: 978-3-7643-7988-9
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