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

Award Information

Agency:
Department of Defense
Branch:
Air Force
Award ID:
17893
Program Year/Program:
1994 / SBIR
Agency Tracking Number:
17893
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Cybernet Systems Corporation
3885 Research Park Drive Ann Arbor, MI 48108-2247
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Woman-Owned: Yes
Minority-Owned: No
HUBZone-Owned: No
 
Phase 2
Fiscal Year: 1994
Title: APPLICATION OF ADAPTIVE AND MODEL-BASED DIAGNOSIS TECHNOLOGY TO SATELLITE SYSTEMS
Agency / Branch: DOD / USAF
Contract: N/A
Award Amount: $250,000.00
 

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.

Principal Investigator:

Charles J. Jacobus
3136682567

Business Contact:

Small Business Information at Submission:

Cybernet Systems Corp
1919 Green Road, Suite B 101 Ann Arbor, MI 48105

EIN/Tax ID: 430924383
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No