Sándor Jenei, Erich Klement, Richard Konzel,
"Interpolation and extrapolation of fuzzy quantities - the multiple-dimensional case"
, in Soft Computing, Vol. 6, Seite(n) 258-270, 2002

Original Titel:

Interpolation and extrapolation of fuzzy quantities - the multiple-dimensional case

Sprache des Titels:

Englisch

Original Kurzfassung:

This paper deals with the problem of rule interpolation and rule extrapolation for fuzzy and possibilistic systems. Such systems are used for representing and processing vague linguistic If-Then-rules, and they have been increasingly applied in the field of control engineering, pattern recognition and expert systems. The methodology of rule interpolation is required for deducing plausible conclusions from sparse (incomplete) rule bases. The interpolation/extrapolation method which was proposed for one-dimensional input space in [4] is extended in this paper to the general n-dimensional case by using the concept of aggregation operators. A characterization of the class of aggregation operators with which the extended method preserves all the nice features of the one- dimensional method is given.

Sprache der Kurzfassung:

Englisch

Englischer Titel:

Interpolation and extrapolation of fuzzy quantities - the multiple-dimensional case

Englische Kurzfassung:

This paper deals with the problem of rule interpolation and rule extrapolation for fuzzy and possibilistic systems. Such systems are used for representing and processing vague linguistic If-Then-rules, and they have been increasingly applied in the field of control engineering, pattern recognition and expert systems. The methodology of rule interpolation is required for deducing plausible conclusions from sparse (incomplete) rule bases. The interpolation/extrapolation method which was proposed for one-dimensional input space in [4] is extended in this paper to the general n-dimensional case by using the concept of aggregation operators. A characterization of the class of aggregation operators with which the extended method preserves all the nice features of the one- dimensional method is given.