APPLICATION OF ADAPTIVE AND MODEL-BASED DIAGNOSIS TECHNOLOGY TO SATELLITE SYSTEMS

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$250,000.00
Award Year:
1994
Program:
SBIR
Phase:
Phase II
Contract:
n/a
Agency Tracking Number:
17893
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
Cybernet Systems Corp
1919 Green Road, Suite B 101, Ann Arbor, MI, 48105
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
Charles J. Jacobus
(313) 668-2567
Business Contact:
() -
Research Institution:
n/a
Abstract
MUCH RECENT WORK HAS BEEN FOCUSED ON ELUCIDATING THE POTENTIAL BENEFITS OF USING FUZZY LOGIC AND ADAPTIVE NETWORK DECISION MAKING METHODS. ADAPTIVE METHODS, LIKE BACK-PROPAGATION TAUGHT NEURAL NETWORKS, CAN MAKE IT POSSIBLE TO BUILD COMPLEX DECISION MAKING SYSTEM WHICH AVOID THE "EXPERT SYSTEMS" PROBLEM OF MANUALLY ACQUIRED KNOWLEDGE "CAPTURE." INSTEAD, THESE ADAPTIVE METHODS CAPTURE KNOWLEDGE BY "LEARNING" FROM EXAMPLE DATASETS WHICH RELATE INPUTS TO DESIRED OUTPUTS (SUCH AS THE DECISION OF A SEISMIC EXPERT). UNFORTUNATELY, VERY LITTLE IS KNOWN ABOUT HOW TO STRUCTURE NEURAL NETWORKS OPTIMALLY FOR PARTICULAR DECISION MAKING TASKS, HOW MUCH TRAINING IS NECESSARY FOR ADEQUATE NETWORK PERFORMANCE, AND HOW TO VALIDATE TRAINED NETWORK PERFORMANCE RELATIVE TO SPECIFICATIONS. WE PROPOSE IMPLEMENTING A SUPERCOMPUTER NETWORK TRAINING, DECISION MAKING, AND VALIDATION TESTBED WHICH WILL INCORPORATE A VARIETY OF POPULAR ADAPTIVE NETWORK MODELS, AND WILL PROVIDE A MEANS FOR TESTING SOME PROPOSED CONCEPTS FOR NETWORK SYNTHESIS WHICH MAY MAKE DESIGNING ADAPTIVE METWORKS TO A SPECIFICATION MORE EASILY DONE FOR IMAGE DATA INTERPRETATION.

* information listed above is at the time of submission.

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