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Proceedings Paper

Reinforcement and unsupervised learning in fuzzy-neuro controllers
Author(s): Emdad Khan
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Paper Abstract

Refinement of the performance of approximate reasoning based controllers (e.g., fuzzy logic based controllers) by using reinforcement (also known as graded) learning have been proposed recently. However, reinforcement learning schemes known today have problems in learning and generating proper control inputs, especially, for complex plants. In this paper, we have presented novel schemes to alleviate these problems found in the existing reinforcement learning based controllers by using unsupervised learning and neuro-fuzzy approach.

Paper Details

Date Published: 1 July 1992
PDF: 10 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140124
Show Author Affiliations
Emdad Khan, National Semiconductor Corp. (United States)

Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)

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