On combination of wavelet transformation and stabilized KH interpolation for fuzzy inferences based on high dimensional sampled functions

Ferenc Lilik, Szilvia Nagy, L. Kóczy

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

A new approach for inference based on treating sampled functions is presented. Sampled functions can be transformed into only a few points by wavelet analysis, thus the complete function is represented by these several discrete points. The finiteness of the teaching samples and the resulting sparse rule bases can be handled by fuzzy rule interpolation methods, like KH interpolation. Using SHDSL transmission performance prediction as an example, the simplification of inference problems based on large, sampled vectors by wavelet transformation and fuzzy rule interpolation applied on these vectors are introduced in this paper.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages31-42
Number of pages12
DOIs
Publication statusPublished - Jan 1 2018

Publication series

NameStudies in Computational Intelligence
Volume758
ISSN (Print)1860-949X

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Keywords

  • Fuzzy inference
  • Fuzzy rule interpolation
  • Performance prediction
  • Wavelet analysis

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Lilik, F., Nagy, S., & Kóczy, L. (2018). On combination of wavelet transformation and stabilized KH interpolation for fuzzy inferences based on high dimensional sampled functions. In Studies in Computational Intelligence (pp. 31-42). (Studies in Computational Intelligence; Vol. 758). Springer Verlag. https://doi.org/10.1007/978-3-319-74681-4_3