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Transparency and simplification of rule-based models for on-line adaptation

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conference contribution
posted on 2012-08-01, 09:03 authored by Richard BuswellRichard Buswell, Plamen Angelov, Jonathan WrightJonathan Wright
One of the principal advantages of fuzzy rule based models over black-box approaches such as Neural Networks or polynomial models is transparency. The linguistic concept associated with the membership functions related to measured variables results in rules that are ‘readable’. This quality is useful in analysing the functionality of processes through the model generated by data mining techniques. The greater the number of rules and the less descriptive the linguistic terms, the less transparent the model. The fewer rules, however, inevitably reduces the model precision with respect to the modelled process. This paper investigates the properties of Takagi-Sugeno models with either a linear function or singleton consequent with respect to model precision and transparency. The study is focused on a ‘steady-state’ heatexchanger model applied to the air-cooling process commonly found in heating, ventilating and air-conditioning (HVAC) equipment. The similarity measures are suitable to application to the on-line generation of these models.

History

School

  • Architecture, Building and Civil Engineering

Citation

BUSWELL, R.A., ANGELOV, P. and WRIGHT, J.A., 2001. Transparency and simplification of rule-based models for on-line adaptation. IN: Garibaldi, J.M. and John, R.I. (eds.) Proceedings of the 2nd International Conference in Fuzzy Logic and Technology [2nd EUSFLAT], Leicester, UK, 5-7 September, pp. 234 - 237

Publisher

© Michael Ley (University of Trier, Informatik, ley@uni-trier.de) and Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Version

  • AM (Accepted Manuscript)

Publication date

2001

Notes

This is a conference paper.

Language

  • en

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