Multistrategy learning approaches to generate and tune fuzzy control structures and their application in manufacturing

C. Egresits, L. Monostori, J. Hornyák

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Intelligence is strongly connected with learning adapting abilities, therefore such capabilities are considered as indispensable features of intelligent manufacturing systems (IMSs). A number of approaches have been described to apply different machine learning (ML) techniques for manufacturing problems, starting with rule induction in symbolic domains and pattern recognition techniques in numerical, subsymbolic domains. In recent years, artificial neural network (ANN) based learning is the dominant ML technique in manufacturing. However, mainly because of the 'black box' nature of ANNs, these solutions have limited industrial acceptance. In the paper, the integration of neural and fuzzy techniques is treated and former solutions are analysed. A genetic algorithm (GA) based approach is introduced to overcome problems that are experienced during manufacturing applications with other algorithms.

Original languageEnglish
Pages (from-to)323-329
Number of pages7
JournalJournal of Intelligent Manufacturing
Volume9
Issue number4
Publication statusPublished - 1998

Fingerprint

Fuzzy control
Learning systems
Pattern recognition
Genetic algorithms
Neural networks

Keywords

  • Genetic algorithms
  • Intelligent manufacturing
  • Machine learning
  • Neuro-fuzzy systems

ASJC Scopus subject areas

  • Artificial Intelligence
  • Industrial and Manufacturing Engineering

Cite this

Multistrategy learning approaches to generate and tune fuzzy control structures and their application in manufacturing. / Egresits, C.; Monostori, L.; Hornyák, J.

In: Journal of Intelligent Manufacturing, Vol. 9, No. 4, 1998, p. 323-329.

Research output: Contribution to journalArticle

@article{e81e3a7470204f40ad9d5617b4229ebc,
title = "Multistrategy learning approaches to generate and tune fuzzy control structures and their application in manufacturing",
abstract = "Intelligence is strongly connected with learning adapting abilities, therefore such capabilities are considered as indispensable features of intelligent manufacturing systems (IMSs). A number of approaches have been described to apply different machine learning (ML) techniques for manufacturing problems, starting with rule induction in symbolic domains and pattern recognition techniques in numerical, subsymbolic domains. In recent years, artificial neural network (ANN) based learning is the dominant ML technique in manufacturing. However, mainly because of the 'black box' nature of ANNs, these solutions have limited industrial acceptance. In the paper, the integration of neural and fuzzy techniques is treated and former solutions are analysed. A genetic algorithm (GA) based approach is introduced to overcome problems that are experienced during manufacturing applications with other algorithms.",
keywords = "Genetic algorithms, Intelligent manufacturing, Machine learning, Neuro-fuzzy systems",
author = "C. Egresits and L. Monostori and J. Horny{\'a}k",
year = "1998",
language = "English",
volume = "9",
pages = "323--329",
journal = "Journal of Intelligent Manufacturing",
issn = "0956-5515",
publisher = "Springer Netherlands",
number = "4",

}

TY - JOUR

T1 - Multistrategy learning approaches to generate and tune fuzzy control structures and their application in manufacturing

AU - Egresits, C.

AU - Monostori, L.

AU - Hornyák, J.

PY - 1998

Y1 - 1998

N2 - Intelligence is strongly connected with learning adapting abilities, therefore such capabilities are considered as indispensable features of intelligent manufacturing systems (IMSs). A number of approaches have been described to apply different machine learning (ML) techniques for manufacturing problems, starting with rule induction in symbolic domains and pattern recognition techniques in numerical, subsymbolic domains. In recent years, artificial neural network (ANN) based learning is the dominant ML technique in manufacturing. However, mainly because of the 'black box' nature of ANNs, these solutions have limited industrial acceptance. In the paper, the integration of neural and fuzzy techniques is treated and former solutions are analysed. A genetic algorithm (GA) based approach is introduced to overcome problems that are experienced during manufacturing applications with other algorithms.

AB - Intelligence is strongly connected with learning adapting abilities, therefore such capabilities are considered as indispensable features of intelligent manufacturing systems (IMSs). A number of approaches have been described to apply different machine learning (ML) techniques for manufacturing problems, starting with rule induction in symbolic domains and pattern recognition techniques in numerical, subsymbolic domains. In recent years, artificial neural network (ANN) based learning is the dominant ML technique in manufacturing. However, mainly because of the 'black box' nature of ANNs, these solutions have limited industrial acceptance. In the paper, the integration of neural and fuzzy techniques is treated and former solutions are analysed. A genetic algorithm (GA) based approach is introduced to overcome problems that are experienced during manufacturing applications with other algorithms.

KW - Genetic algorithms

KW - Intelligent manufacturing

KW - Machine learning

KW - Neuro-fuzzy systems

UR - http://www.scopus.com/inward/record.url?scp=0032131589&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032131589&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0032131589

VL - 9

SP - 323

EP - 329

JO - Journal of Intelligent Manufacturing

JF - Journal of Intelligent Manufacturing

SN - 0956-5515

IS - 4

ER -