Machine self-teaching methods for parameter optimization. Final report, October 1984-August 1986
The problem of determining near-optimum parameter-control logic is addressed for cases where a sensor or communication system is highly flexible and the logic cannot be determined analytically. A system that supports human-like learning of optimum parameters is outlined. The major subsystems are (1) a simulation system (described for a radar example), (2) a performance monitoring system, (3) the learning system, and (4) the initial knowledge used by all subsystems. The initial knowledge is expressed modularly as specifications (e.g., radar constraints, performance measures, and target characteristics), relationships (among parameters, intermediate measures, and component performance measures), and formulas. The intent of the learning system is to relieve the human from the very tedious trial-and-error process of examining performance, selecting and applying curve-fitting methods, and selecting the next trial set of parameters. A learning system to design a simple radar meeting specific performance constraints is described in detail, for experimental purposes, in generic object-based code.
- Research Organization:
- Naval Ocean Systems Center, San Diego, CA (United States)
- OSTI ID:
- 5804115
- Report Number(s):
- AD-A-180285/9/XAB; NOSC/TR-1039
- Country of Publication:
- United States
- Language:
- ENGLISH
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Related Subjects
ARTIFICIAL INTELLIGENCE
CONTROL SYSTEMS
COMPUTERIZED SIMULATION
LEARNING
RADAR
AUTOMATION
CONTROL
MONITORING
OPTIMIZATION
SUPPORTS
TARGETS
990210* - Supercomputers- (1987-1989)
990220 - Computers
Computerized Models
& Computer Programs- (1987-1989)