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

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3 Author(s)
Mohammadian, M. ; Dept. of Math. & Comput., Central Queensland Univ., Rockhampton, Qld., Australia ; Yu, X.H. ; Smith, J.D.

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

Published in:

Emerging Technologies and Factory Automation, 1993. Design and Operations of Intelligent Factories. Workshop Proceedings., IEEE 2nd International Workshop on

Date of Conference:

27-29 Sep 1993