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A self-learning rule base for command following in dynamical systemsIn this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules.
Document ID
19930020342
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Tsai, Wei K.
(California Univ. Irvine, CA, United States)
Lee, Hon-Mun
(California Univ. Irvine, CA, United States)
Parlos, Alexander
(Texas A&M Univ. College Station., United States)
Date Acquired
September 6, 2013
Publication Date
December 1, 1992
Publication Information
Publication: NASA. Johnson Space Center, North American Fuzzy Logic Processing Society (NAFIPS 1992), Volume 1
Subject Category
Computer Programming And Software
Accession Number
93N29531
Funding Number(s)
CONTRACT_GRANT: DE-FG07-89ER-12893
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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