A general extension of fuzzy SVD rule base reduction using arbitrary inference algorithm
Péter Bárányi, A. Martinovics, Szilveszter Kovács, Domonkos Tikk, Yeung Yam · 2002
Fuzzy rule base reduction has emerged recently as an important topic of research in fuzzy theories. Main difficulty of any generated rule bases is that the number of rules increases exponentially with the number of variables and fuzzy terms. Singular value decomposition (SVD) based method has been first published for Sugeno algorithm. It was then extended to the Takagi-Sugeno controller, to rule bases with nonsingleton consequents and to fuzzy rule interpolation algorithms. However, the application of these methods are restricted to some special inference engines and rule bases. In this paper we introduce a general SVD-based rule base reduction method for arbitrary rule base, namely arbitrary shaped antecedents, inference algorithm, and consequent sets described by arbitrary (but finite) number of parameters. We demonstrate the use of the proposed method on a control system of automatically guided vehicle.