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Automated fuzzy knowledge acquisition with connectionist adaptation

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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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Authors and Affiliations

  1. Department of Mathematics and Computing, Central Queensland University, QLD 4702, Rockhampton, Australia

    X. H. Yu & J. D. Smith

  2. Department of Computer Science, Edith Cowan University, Perth, Australia

    M. Mohammadian

Authors
  1. X. H. Yu
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  2. J. D. Smith
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  3. M. Mohammadian
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Yu, X.H., Smith, J.D. & Mohammadian, M. Automated fuzzy knowledge acquisition with connectionist adaptation. Neural Comput & Applic 4, 27–34 (1996). https://doi.org/10.1007/BF01413867

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  • DOI: https://doi.org/10.1007/BF01413867

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