Learning the optimal parameter of the Hamacher t-norm applied for fuzzy-rule-based model extraction

László Gál, Rita Lovassy, I. Rudas, L. Kóczy

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Mamdani-type inference systems with trapezoidal-shaped fuzzy membership functions play a crucial role in a wide variety of engineering systems, including real-time control, transportation and logistics, network management, etc. The automatic identification or construction of such fuzzy systems input output data is one of the key problems in modeling. In the past years, the authors have investigated several different fuzzy t-norms, among others, algebraic and trigonometric ones, and the Hamacher product by substituting the standard "min" t-norm operation, in order to achieve better model fitting. In the present paper, the focus is on examining the general parametric Hamacher t-norm, where the free parameter quite essentially influences the quality of modeling and the learning capability of the model identification system. Based on a wide scope of simulation experiments, a quasi-optimal interval for the value of the Hamacher operator is proposed.

Original languageEnglish
Pages (from-to)133-142
Number of pages10
JournalNeural Computing and Applications
Volume24
Issue number1
DOIs
Publication statusPublished - Jan 2014

Fingerprint

Fuzzy rules
Identification (control systems)
Network management
Real time control
Fuzzy systems
Membership functions
Systems engineering
Logistics
Experiments

Keywords

  • Aggregation operators
  • Fuzzy-rule-based model
  • Hamacher t-norm
  • Improved bacterial memetic algorithm
  • Mamdani inference system
  • Modified bacterial memetic algorithm

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Learning the optimal parameter of the Hamacher t-norm applied for fuzzy-rule-based model extraction. / Gál, László; Lovassy, Rita; Rudas, I.; Kóczy, L.

In: Neural Computing and Applications, Vol. 24, No. 1, 01.2014, p. 133-142.

Research output: Contribution to journalArticle

@article{6cb344d90854496eb8524459d203fdb4,
title = "Learning the optimal parameter of the Hamacher t-norm applied for fuzzy-rule-based model extraction",
abstract = "Mamdani-type inference systems with trapezoidal-shaped fuzzy membership functions play a crucial role in a wide variety of engineering systems, including real-time control, transportation and logistics, network management, etc. The automatic identification or construction of such fuzzy systems input output data is one of the key problems in modeling. In the past years, the authors have investigated several different fuzzy t-norms, among others, algebraic and trigonometric ones, and the Hamacher product by substituting the standard {"}min{"} t-norm operation, in order to achieve better model fitting. In the present paper, the focus is on examining the general parametric Hamacher t-norm, where the free parameter quite essentially influences the quality of modeling and the learning capability of the model identification system. Based on a wide scope of simulation experiments, a quasi-optimal interval for the value of the Hamacher operator is proposed.",
keywords = "Aggregation operators, Fuzzy-rule-based model, Hamacher t-norm, Improved bacterial memetic algorithm, Mamdani inference system, Modified bacterial memetic algorithm",
author = "L{\'a}szl{\'o} G{\'a}l and Rita Lovassy and I. Rudas and L. K{\'o}czy",
year = "2014",
month = "1",
doi = "10.1007/s00521-013-1499-3",
language = "English",
volume = "24",
pages = "133--142",
journal = "Neural Computing and Applications",
issn = "0941-0643",
publisher = "Springer London",
number = "1",

}

TY - JOUR

T1 - Learning the optimal parameter of the Hamacher t-norm applied for fuzzy-rule-based model extraction

AU - Gál, László

AU - Lovassy, Rita

AU - Rudas, I.

AU - Kóczy, L.

PY - 2014/1

Y1 - 2014/1

N2 - Mamdani-type inference systems with trapezoidal-shaped fuzzy membership functions play a crucial role in a wide variety of engineering systems, including real-time control, transportation and logistics, network management, etc. The automatic identification or construction of such fuzzy systems input output data is one of the key problems in modeling. In the past years, the authors have investigated several different fuzzy t-norms, among others, algebraic and trigonometric ones, and the Hamacher product by substituting the standard "min" t-norm operation, in order to achieve better model fitting. In the present paper, the focus is on examining the general parametric Hamacher t-norm, where the free parameter quite essentially influences the quality of modeling and the learning capability of the model identification system. Based on a wide scope of simulation experiments, a quasi-optimal interval for the value of the Hamacher operator is proposed.

AB - Mamdani-type inference systems with trapezoidal-shaped fuzzy membership functions play a crucial role in a wide variety of engineering systems, including real-time control, transportation and logistics, network management, etc. The automatic identification or construction of such fuzzy systems input output data is one of the key problems in modeling. In the past years, the authors have investigated several different fuzzy t-norms, among others, algebraic and trigonometric ones, and the Hamacher product by substituting the standard "min" t-norm operation, in order to achieve better model fitting. In the present paper, the focus is on examining the general parametric Hamacher t-norm, where the free parameter quite essentially influences the quality of modeling and the learning capability of the model identification system. Based on a wide scope of simulation experiments, a quasi-optimal interval for the value of the Hamacher operator is proposed.

KW - Aggregation operators

KW - Fuzzy-rule-based model

KW - Hamacher t-norm

KW - Improved bacterial memetic algorithm

KW - Mamdani inference system

KW - Modified bacterial memetic algorithm

UR - http://www.scopus.com/inward/record.url?scp=84891888356&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84891888356&partnerID=8YFLogxK

U2 - 10.1007/s00521-013-1499-3

DO - 10.1007/s00521-013-1499-3

M3 - Article

AN - SCOPUS:84891888356

VL - 24

SP - 133

EP - 142

JO - Neural Computing and Applications

JF - Neural Computing and Applications

SN - 0941-0643

IS - 1

ER -