A Parallel Fuzzy Filter Network for Pattern Recognition

Balazs Tusor, A. Várkonyi-Kóczy, József Bukor

Research output: Chapter


Nowadays, parallelization is an increasingly popular tool to speed up algorithms. Data classification is one of the many fields of computer science that can take significant advantage of that. In this paper, a parallel implementation of Fuzzy RBF based filters are proposed for pattern recognition problems. It realizes a simple pattern matching by using the radial basis functions for proximity detection, then simply choosing the class or label associated to the pattern as output. The classifier has the advantage of being very simple to implement, to train and to modify the obtained knowledge. With the parallel computing improvement, the speed of both the training and evaluation phase are significantly increased compared to the sequential implementation.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
Number of pages8
Publication statusPublished - jan. 1 2019

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Control and Systems Engineering

Cite this

Tusor, B., Várkonyi-Kóczy, A., & Bukor, J. (2019). A Parallel Fuzzy Filter Network for Pattern Recognition. In Lecture Notes in Networks and Systems (pp. 275-282). (Lecture Notes in Networks and Systems; Vol. 53). Springer. https://doi.org/10.1007/978-3-319-99834-3_36