Automated fuzzy knowledge acquisition with connectionist adaptation

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

doi:10.1007/BF01413867

Automated fuzzy knowledge acquisition with connectionist adaptation

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

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	27-34
Number of pages	8
Journal	Neural Computing and Applications
Volume	4
Issue number	1
DOIs	https://doi.org/10.1007/BF01413867
Publication status	Published - 1 Jan 1996

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10.1007/BF01413867

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KW - Optimisation

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Automated fuzzy knowledge acquisition with connectionist adaptation

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