Merging Partial Fuzzy Rule-bases

Martina Daňková

2020

Abstract

We propose two basic ways of merging various partial fuzzy rule-bases containing knowledge related to the same process or dependency in general. The knowledge that is not at the disposal is considered undefined and encoded using some dummy value. For simplicity, we use only one code for undefined membership value, and we handle the undefined membership values using operations of variable-domain fuzzy set theory, i.e., the theory that allows fuzzy sets to have undefined membership values. Moreover, we study one of the essential properties in fuzzy modeling–a graded property of functionality. We provide estimations for degrees of the functionality of input models and merged models of partial fuzzy rule-bases.

Paper Citation

in Harvard Style

Daňková M. (2020). Merging Partial Fuzzy Rule-bases. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: FCTA; ISBN 978-989-758-475-6, SciTePress, pages 243-251. DOI: 10.5220/0010058302430251

in Bibtex Style

@conference{fcta20,
author={Martina Daňková},
title={Merging Partial Fuzzy Rule-bases},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: FCTA},
year={2020},
pages={243-251},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010058302430251},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: FCTA
TI - Merging Partial Fuzzy Rule-bases
SN - 978-989-758-475-6
AU - Daňková M.
PY - 2020
SP - 243
EP - 251
DO - 10.5220/0010058302430251
PB - SciTePress