zbMATH — the first resource for mathematics

Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Fuzzy identification of systems and its applications to modeling and control. (English) Zbl 0576.93021
The paper presents a mathematical tool to build a fuzzy model of a system. Using multidimensional fuzzy reasoning suggested by the same authors [Fuzzy Sets Syst. 9, 313--325 (1983; Zbl 0513.94035)], they surprisingly reduce the number of implications, so that fuzzy implication is improved and reasoning is simplified. The presented fuzzy implication is quite simple. It is based on a fuzzy partition of the input space. In each fuzzy subspace a linear input-output relation is formed. The output is given by the aggregation of the values inferred by some implications that were applied to an input. The method of identification of a system using its input-output data is also shown. Practical applications of the proposed method to real industrial processes are presented. The results are fair and suggest applicability of the proposed method.
Reviewer: R.Vachnadze

93B30System identification
93B15Realizability of systems from input-output data
94D05Fuzzy sets and logic in connection with communication
Full Text: DOI