SBIR/STTR Research Award
A HYBRID NEURAL NETWORK/EXPERT SYSTEM ENVIRONMENT
Award amount
$50,000Program and phase
SBIR · Phase IAward year
1991Award details
- Awarding agency
- National Aeronautics and Space Administration
- Branch
- Not reported
- Recipient company
- CHARLES RIVER ANALYTICS, INC.
- Proposal award date
- Not reported
- Contract end date
- Not reported
- UEI
- DD1BYN8SN355
- Solicitation number
- Not reported
- Topic code
- Not reported
Research abstract
THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS (ANNS) AND KNOWLEDGE-BASED EXPERT SYSTEMS IS AN IDEAL STEP IN THE DEVELOPMENT OF INTELLIGENT SYSTEMS. IN GENERAL, THE TWO METHODS COMPLEMENT EACH OTHER, WITH THE ANNS PROVIDING SOFT CONSTRAINTS AND EXPERT SYSTEMS, HARD CONSTRAINTS. SPECIFICALLY, ANNS PERFORM NONLINEAR FUNCTIONS, PATTERN RECOGNITION, FAULT TOLERANCE, AND PARALLEL PROCESSING; EXPERT SYSTEMS INVOLVE LANGUAGE PROCESSING, FORMAL LOGIC, AND RULE INTERPRETATION. THE HYBRID COMBINATION OF ANNS ANDEXPERT SYSTEMS WILL FACILITATE THE AUTOMATION OF VARIOUS SPACE STATION APPLICATIONS WHILE PROVIDING UNSUPERVISED ADAPTABILITY AND REAL-TIME FUNCTIONALITY. THIS PROJECT WILL IDENTIFY AND DEVELOP BASELINE ARCHITECTURE AND REQUIREMENTS SPECIFICATION FOR THE INTEGRATION OF NEURAL NETWORKS AND OF EXPERT SYSTEMS TO FORM A HYBRID SOFTWARE ENVIRONMENT. ADDITIONALLY, STRATEGIES FOR THE IMPLEMENTATION OF INTELLIGENT SYSTEMS WITHIN THE HYBRID ENVIRONMENT WILL BE INVESTIGATED FOR SPACE STATION CONTROL AND SENSOR MONITORING APPLICATIONS. THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS (ANNS) AND KNOWLEDGE-BASED EXPERT SYSTEMS IS AN IDEAL STEP IN THE DEVELOPMENT OF INTELLIGENT SYSTEMS. IN GENERAL, THE TWO METHODS COMPLEMENT EACH OTHER, WITH THE ANNS PROVIDING SOFT CONSTRAINTS AND EXPERT SYSTEMS, HARD CONSTRAINTS. SPECIFICALLY, ANNS PERFORM NONLINEAR FUNCTIONS, PATTERN RECOGNITION, FAULT TOLERANCE, AND PARALLEL PROCESSING; EXPERT SYSTEMS INVOLVE LANGUAGE PROCESSING, FORMAL LOGIC, AND RULE INTERPRETATION. THE HYBRID COMBINATION OF ANNS ANDEXPERT SYSTEMS WILL FACILITATE THE AUTOMATION OF VARIOUS SPACE STATION APPLICATIONS WHILE PROVIDING UNSUPERVISED ADAPTABILITY AND REAL-TIME FUNCTIONALITY. THIS PROJECT WILL IDENTIFY AND DEVELOP BASELINE ARCHITECTURE AND REQUIREMENTS SPECIFICATION FOR THE INTEGRATION OF NEURAL NETWORKS AND OF EXPERT SYSTEMS TO FORM A HYBRID SOFTWARE ENVIRONMENT. ADDITIONALLY, STRATEGIES FOR THE IMPLEMENTATION OF INTELLIGENT SYSTEMS WITHIN THE HYBRID ENVIRONMENT WILL BE INVESTIGATED FOR SPACE STATION CONTROL AND SENSOR MONITORING APPLICATIONS.
Technology keywords
Not reported
Other awards by this recipient
Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.
- Neural Network Enhancements for Customized Reduced Order Models to Accelerate Nuanced COA Evaluation and Reasoning (NECROMANCER)
- Foreign malign influence (FMI) AI-driven Challenge Environment eXperiential Simulation (FACE-X)
- Forecasting Outcomes with Root Cause Evaluation for Fielded Systems (FORCEFIELDS)
- Anthropometry and Pose Observation using Low-dimensional Latent Optimization (APOLLO)
- Robotic Interconnecting Vessels Enabling River-crossing using Sensor Processing and Autonomous Navigation (RIVERSPAN)
Research funding history
961 SBIR/STTR records share this UEI (1984–2026). Reported award amounts total $386,500,110. Reported amounts are not federal obligations.
Phases: Phase I, Phase II, Phase II – Direct to Phase II
Agencies: Department of Defense, National Aeronautics and Space Administration, Department of Health and Human Services, Department of Homeland Security, Department of Transportation, Department of Energy