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NEURAL NETWORKS TO MONITOR SPACE STATION DISTRIBUTED SYSTEM

Award Information

Agency:
National Aeronautics and Space Administration
Branch:
N/A
Award ID:
16942
Program Year/Program:
1991 / SBIR
Agency Tracking Number:
16942
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Als Co
3210a Wheaton Way Elicott City, MD 21043
View profile »
Woman-Owned: No
Minority-Owned: Yes
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 1991
Title: NEURAL NETWORKS TO MONITOR SPACE STATION DISTRIBUTED SYSTEM
Agency: NASA
Contract: N/A
Award Amount: $48,200.00
 

Abstract:

NEURAL NETWORKS WILL BE APPLIED TO MONITOR DISTRIBUTED SYSTEMS ON SPACE STATION, WITH THE EVENTUAL GOAL OF DEVELOPING HYBRID NEURAL NETWORK, EXPERT SYSTEMS, AND CONVENTIONAL SOFTWARE SYSTEMS TO MONITOR AND TO CONTROL COMPLICATED SYSTEMS. THE PHASE I EFFORT WILL CONCENTRATE ON USING A NETWORK TO MONITOR ONE DISTRIBUTED SYSTEM AND TO DIFFERENTIATE BETWEEN NORMAL AND ABNORMAL OPERATING CONDITIONS, AND ON ANALYZING THE TRAINED NETWORK TO DERIVE ITS STRATEGIES. THE CENTRAL THERMAL BUS WILL BE SIMULATED, AND THE RESULTING MODEL WILL PROVIDE SIMULATED SENSOR DATA FOR NETWORK TRAINING AND TESTING. NORMAL OPERATING CONDITIONS WILL BE SIMULATED, AS WELL AS SPECIFIC SYSTEM FAILURES. SEVERAL NETWORKS WILL BE TRAINED USING VARIOUS TRANSFORMS OF SENSOR INPUT, AND THE BEST NETWORK WILL BE SELECTED FOR ANALYSIS. DIRECT OBSERVATION AND CALCULATION OF THE RESPONSE OF EACH NODE IN THE NETWORK TO VARIOUS INPUT PATTERNS WILL BE USED TO DERIVE NETWORK STRATEGIES. THE SUCCESSFUL TECHNIQUES FOR MONITORING COMPLICATED SYSTEMSCAN BE EXTENDED TO BECOME DISTRIBUTED SYSTEMS IN SPACE WITH HUMAN-LIKE INTELLIGENCE. FURTHER APPLICATIONS IN PROCESS CONTROL AND MILITARY SYSTEMS ARE EXPECTED. NEURAL NETWORKS WILL BE APPLIED TO MONITOR DISTRIBUTED SYSTEMS ON SPACE STATION, WITH THE EVENTUAL GOAL OF DEVELOPING HYBRID NEURAL NETWORK, EXPERT SYSTEMS, AND CONVENTIONAL SOFTWARE SYSTEMS TO MONITOR AND TO CONTROL COMPLICATED SYSTEMS. THE PHASE I EFFORT WILL CONCENTRATE ON USING A NETWORK TO MONITOR ONE DISTRIBUTED SYSTEM AND TO DIFFERENTIATE BETWEEN NORMAL AND ABNORMAL OPERATING CONDITIONS, AND ON ANALYZING THE TRAINED NETWORK TO DERIVE ITS STRATEGIES. THE CENTRAL THERMAL BUS WILL BE SIMULATED, AND THE RESULTING MODEL WILL PROVIDE SIMULATED SENSOR DATA FOR NETWORK TRAINING AND TESTING. NORMAL OPERATING CONDITIONS WILL BE SIMULATED, AS WELL AS SPECIFIC SYSTEM FAILURES. SEVERAL NETWORKS WILL BE TRAINED USING VARIOUS TRANSFORMS OF SENSOR INPUT, AND THE BEST NETWORK WILL BE SELECTED FOR ANALYSIS. DIRECT OBSERVATION AND CALCULATION OF THE RESPONSE OF EACH NODE IN THE NETWORK TO VARIOUS INPUT PATTERNS WILL BE USED TO DERIVE NETWORK STRATEGIES. THE SUCCESSFUL TECHNIQUES FOR MONITORING COMPLICATED SYSTEMSCAN BE EXTENDED TO BECOME DISTRIBUTED SYSTEMS IN SPACE WITH HUMAN-LIKE INTELLIGENCE. FURTHER APPLICATIONS IN PROCESS CONTROL AND MILITARY SYSTEMS ARE EXPECTED.

Principal Investigator:


0

Business Contact:

Small Business Information at Submission:

Als Co
3210a Wheaton Way Elicott City, MD 21043

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