Automated fuzzy knowledge acquisition with connectionist adaptation

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper presents an automated knowledge acquisition architecture for the truck docking problem. The architecture consists of a neural network block, a fuzzy rule generation block and a genetic optimisation block. The neural network block is used to quickly and adaptively learn from trials the driving knowledge. The fuzzy rule generation block then extracts the driving knowledge to form a knowledge rule base. The driving knowledge rule base is further optimised in the genetic optimisation block using a genetic algorithm. Computer simulations are presented to show the effectiveness of the architecture.

Original languageEnglish
Pages (from-to)27-34
Number of pages8
JournalNeural Computing and Applications
Volume4
Issue number1
DOIs
Publication statusPublished - 1 Jan 1996

Fingerprint

Knowledge acquisition
Fuzzy rules
Neural networks
Trucks
Genetic algorithms
Computer simulation

Cite this

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abstract = "This paper presents an automated knowledge acquisition architecture for the truck docking problem. The architecture consists of a neural network block, a fuzzy rule generation block and a genetic optimisation block. The neural network block is used to quickly and adaptively learn from trials the driving knowledge. The fuzzy rule generation block then extracts the driving knowledge to form a knowledge rule base. The driving knowledge rule base is further optimised in the genetic optimisation block using a genetic algorithm. Computer simulations are presented to show the effectiveness of the architecture.",
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Automated fuzzy knowledge acquisition with connectionist adaptation. / Yu, X. H.; Smith, J. D.; Mohammadian, M.

In: Neural Computing and Applications, Vol. 4, No. 1, 01.01.1996, p. 27-34.

Research output: Contribution to journalArticle

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