A case study of knowledge acquisition: From connectionist learning to an optimized fuzzy knowledge base

Masoud Mohammadian; X. H Yu; J. D Smith

doi:10.1109/ETFA.1993.396423

A case study of knowledge acquisition: From connectionist learning to an optimized fuzzy knowledge base

Masoud Mohammadian
, X. H Yu
, J. D Smith

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

4 Citations (Scopus)

85 Downloads (Pure)

Abstract

An automated knowledge acquisition architecture for docking a truck problem is presented. The architecture consists of a neural network controller a fuzzy rule maker, and a fuzzy controller. The neural network controller is used to learn the driving knowledge from trials. The driving knowledge is then extracted by the fuzzy rule maker to form a driving knowledge rule base. The driving knowledge rule base is further optimized using a genetic algorithm. Computer simulations are presented to show the effectiveness of the architecture.

Original language	English
Title of host publication	Proceedings of IEEE 2nd International Workshop on Emerging Technologies and Factory Automation
Subtitle of host publication	Design and Operations of Intelligent Factories, ETFA 1993
Place of Publication	Australia
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	106-111
Number of pages	6
Volume	1
Edition	1
ISBN (Print)	0780309853
DOIs	https://doi.org/10.1109/ETFA.1993.396423 https://doi.org/0.1109/ETFA.1993.396423
Publication status	Published - 1993
Event	2nd IEEE International Workshop on Emerging Technologies and Factory Automation: Design and Operations of Intelligent Factories, ETFA 1993 - Palm Cove-Cairns, Australia Duration: 27 Sept 1993 → 29 Sept 1993

Conference

Conference	2nd IEEE International Workshop on Emerging Technologies and Factory Automation: Design and Operations of Intelligent Factories, ETFA 1993
Country/Territory	Australia
City	Palm Cove-Cairns
Period	27/09/93 → 29/09/93

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10.1109/ETFA.1993.396423
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https://ieeexplore.ieee.org/document/396423Licence: Other

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Mohammadian, M., Yu, X. H., & Smith, J. D. (1993). A case study of knowledge acquisition: From connectionist learning to an optimized fuzzy knowledge base. In Proceedings of IEEE 2nd International Workshop on Emerging Technologies and Factory Automation: Design and Operations of Intelligent Factories, ETFA 1993 (1 ed., Vol. 1, pp. 106-111). Article 396423 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ETFA.1993.396423, https://doi.org/0.1109/ETFA.1993.396423

Mohammadian, Masoud ; Yu, X. H ; Smith, J. D. / A case study of knowledge acquisition: From connectionist learning to an optimized fuzzy knowledge base. Proceedings of IEEE 2nd International Workshop on Emerging Technologies and Factory Automation: Design and Operations of Intelligent Factories, ETFA 1993. Vol. 1 1. ed. Australia : IEEE, Institute of Electrical and Electronics Engineers, 1993. pp. 106-111

@inproceedings{710029535ead422182b8642b4af1e751,

title = "A case study of knowledge acquisition: From connectionist learning to an optimized fuzzy knowledge base",

abstract = "An automated knowledge acquisition architecture for docking a truck problem is presented. The architecture consists of a neural network controller a fuzzy rule maker, and a fuzzy controller. The neural network controller is used to learn the driving knowledge from trials. The driving knowledge is then extracted by the fuzzy rule maker to form a driving knowledge rule base. The driving knowledge rule base is further optimized using a genetic algorithm. Computer simulations are presented to show the effectiveness of the architecture.",

keywords = "knowledge acquisition, Neural Networks, Learning, Adaptive Systems, Control, Robotics",

author = "Masoud Mohammadian and Yu, \{X. H\} and Smith, \{J. D\}",

year = "1993",

doi = "10.1109/ETFA.1993.396423",

language = "English",

isbn = "0780309853",

volume = "1",

pages = "106--111",

booktitle = "Proceedings of IEEE 2nd International Workshop on Emerging Technologies and Factory Automation",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

edition = "1",

note = "2nd IEEE International Workshop on Emerging Technologies and Factory Automation: Design and Operations of Intelligent Factories, ETFA 1993 ; Conference date: 27-09-1993 Through 29-09-1993",

}

Mohammadian, M, Yu, XH & Smith, JD 1993, A case study of knowledge acquisition: From connectionist learning to an optimized fuzzy knowledge base. in Proceedings of IEEE 2nd International Workshop on Emerging Technologies and Factory Automation: Design and Operations of Intelligent Factories, ETFA 1993. 1 edn, vol. 1, 396423, IEEE, Institute of Electrical and Electronics Engineers, Australia, pp. 106-111, 2nd IEEE International Workshop on Emerging Technologies and Factory Automation: Design and Operations of Intelligent Factories, ETFA 1993, Palm Cove-Cairns, Australia, 27/09/93. https://doi.org/10.1109/ETFA.1993.396423, https://doi.org/0.1109/ETFA.1993.396423

A case study of knowledge acquisition: From connectionist learning to an optimized fuzzy knowledge base. / Mohammadian, Masoud; Yu, X. H; Smith, J. D.
Proceedings of IEEE 2nd International Workshop on Emerging Technologies and Factory Automation: Design and Operations of Intelligent Factories, ETFA 1993. Vol. 1 1. ed. Australia: IEEE, Institute of Electrical and Electronics Engineers, 1993. p. 106-111 396423.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - A case study of knowledge acquisition: From connectionist learning to an optimized fuzzy knowledge base

AU - Mohammadian, Masoud

AU - Yu, X. H

AU - Smith, J. D

PY - 1993

Y1 - 1993

N2 - An automated knowledge acquisition architecture for docking a truck problem is presented. The architecture consists of a neural network controller a fuzzy rule maker, and a fuzzy controller. The neural network controller is used to learn the driving knowledge from trials. The driving knowledge is then extracted by the fuzzy rule maker to form a driving knowledge rule base. The driving knowledge rule base is further optimized using a genetic algorithm. Computer simulations are presented to show the effectiveness of the architecture.

AB - An automated knowledge acquisition architecture for docking a truck problem is presented. The architecture consists of a neural network controller a fuzzy rule maker, and a fuzzy controller. The neural network controller is used to learn the driving knowledge from trials. The driving knowledge is then extracted by the fuzzy rule maker to form a driving knowledge rule base. The driving knowledge rule base is further optimized using a genetic algorithm. Computer simulations are presented to show the effectiveness of the architecture.

KW - knowledge acquisition

KW - Neural Networks

KW - Learning

KW - Adaptive Systems

KW - Control

KW - Robotics

UR - https://www.scopus.com/pages/publications/0007506651

U2 - 10.1109/ETFA.1993.396423

DO - 10.1109/ETFA.1993.396423

M3 - Conference contribution

AN - SCOPUS:0007506651

SN - 0780309853

VL - 1

SP - 106

EP - 111

BT - Proceedings of IEEE 2nd International Workshop on Emerging Technologies and Factory Automation

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Australia

T2 - 2nd IEEE International Workshop on Emerging Technologies and Factory Automation: Design and Operations of Intelligent Factories, ETFA 1993

Y2 - 27 September 1993 through 29 September 1993

ER -

Mohammadian M, Yu XH, Smith JD. A case study of knowledge acquisition: From connectionist learning to an optimized fuzzy knowledge base. In Proceedings of IEEE 2nd International Workshop on Emerging Technologies and Factory Automation: Design and Operations of Intelligent Factories, ETFA 1993. 1 ed. Vol. 1. Australia: IEEE, Institute of Electrical and Electronics Engineers. 1993. p. 106-111. 396423 doi: 10.1109/ETFA.1993.396423, 0.1109/ETFA.1993.396423

A case study of knowledge acquisition: From connectionist learning to an optimized fuzzy knowledge base

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