Abstract
In Mamdani systems, redundancy of fuzzy rule bases that derives from extensive sharing of a limited number of output membership functions among the rules, is often an overlooked property. In current study, means for detection and removal of such kind redundancy have been developed. Our experiments with case studies collected from literature and Mackey-Glass time series prediction models show error-free rule base reduction by 30-60% that partially cures the curse of dimensionality problem characteristic to fuzzy systems.
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Riid, A., Saastamoinen, K., Rüstern, E. (2010). Redundancy Detection and Removal Tool for Transparent Mamdani Systems. In: Sgurev, V., Hadjiski, M., Kacprzyk, J. (eds) Intelligent Systems: From Theory to Practice. Studies in Computational Intelligence, vol 299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13428-9_19
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DOI: https://doi.org/10.1007/978-3-642-13428-9_19
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