Machine learning of parameter control doctrine for sensor and communication systems. Final report
Technical Report
·
OSTI ID:6781209
Artificial-intelligence approaches to learning were reviewed for their potential contributions to the construction of a system to learn parameter-control doctrine. Separate learning tasks were isolated and several levels of related problems were distinguished. Formulas for providing the learning system with measures of its performance were derived for four kinds of targets.
- Research Organization:
- Naval Ocean Systems Center, San Diego, CA (USA)
- OSTI ID:
- 6781209
- Report Number(s):
- AD-A-200096/6/XAB; NOSC/TR-1220
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
42 ENGINEERING
ARTIFICIAL INTELLIGENCE
COMMUNICATIONS
CONTROL SYSTEMS
AIR
LEARNING
PROGRESS REPORT
DOCUMENT TYPES
FLUIDS
GASES
990210* - Supercomputers- (1987-1989)
420800 - Engineering- Electronic Circuits & Devices- (-1989)
42 ENGINEERING
ARTIFICIAL INTELLIGENCE
COMMUNICATIONS
CONTROL SYSTEMS
AIR
LEARNING
PROGRESS REPORT
DOCUMENT TYPES
FLUIDS
GASES
990210* - Supercomputers- (1987-1989)
420800 - Engineering- Electronic Circuits & Devices- (-1989)