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Evolving rule-based models: a tool for intelligent adaptation

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conference contribution
posted on 27.07.2012, 13:50 by Plamen Angelov, Richard Buswell
An online approach for rule-base evolution by recursive adaptation of rule structure and parameters is described . An integral part of the procedure is to maximise the model transparency by simplifying the fuzzy linguistic descriptions of the input variables. The rule base evolves over time, utilising direct calculation approaches and hence minimising the reliance on the use of computationally expensive techniques, such as genetic algorithms. An online version of subtractive clustering recently introduced by the authors (P.P. Angelov and R.A. Buswell) is used for determination of the antecedent part of the fuzzy rules. Recursive least squares estimation is employed to determine the parameters of the consequent part of each rule. The use of these efficient non-iterative techniques is the key to the low computational demands of the algorithm. The application of similarity measures improves the interpretability and compactness of the resulting eR model, with no significant detriment to the model precision. A time series prediction problem on data from a real indoor climate control (ICC) system has been considered to test and validate the proposed model simplification method.

History

School

  • Architecture, Building and Civil Engineering

Citation

ANGELOV, P. and BUSWELL, R.A., 2001. Evolving rule-based models: a tool for intelligent adaptation. IN: Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference 2011, Volume 2, pp. 1062 - 1067

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2001

Notes

This paper [© IEEE] was presented at the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, 25-28 July 2001. It is also available at: http://ieeexplore.ieee.org/. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISBN

0780370783

Language

en

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