Actions for Rule-based mechanisms of learning for intelligent adaptive flight control

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Rule-based mechanisms of learning for intelligent adaptive flight control

Author
Stengel, Robert F.
Published
Mar 1, 1990.
Physical Description
1 electronic document
Additional Creators
Handelman, David A.
Online Version

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Unclassified, Unlimited, Publicly available.
Free-to-read Unrestricted online access
Summary
How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.
Other Subject(s)
Collection
NASA Technical Reports Server (NTRS) Collection.
Note
Document ID: 19900011623.
Accession ID: 90N20939.
NASA, Langley Research Center, Joint University Program for Air Transportation Research, 1988-1989; p 189-194.
Terms of Use and Reproduction
No Copyright.
View MARC record | catkey: 15686868