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A HYBRID NEURAL NETWORK/EXPERT SYSTEM APPROACH TO REMOTE SENSING

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: N/A
Agency Tracking Number: 16077
Amount: $50,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 1992
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
55 Wheeler St
Cambridge, MA 02123
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 James M. Mazzu
 () -
Business Contact
Phone: () -
Research Institution
N/A
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 SUCH THAT ANNS PROVIDE 'SOFT' CONSTRAINTS, WHILE EXPERT SYSTEMS ALLOW 'HARD' CONSTRAINTS. SPECIFICALLY, ANNS PERFORM NONLINEAR FUNCTIONS, PATTERN RECOGNITION CAPABILITIES, FAULT TOLERANCE AND PARALLEL PROCESSING; WHILE EXPERT SYSTEMS INVOLVE LANGUAGE PROCESSING, FORMAL LOGIC AND RULE INTERPRETATION. HERE, WE INTEND TO EXPLOIT THE COMPLEMENTARY STRENGTHS OF NEURAL NETWORKS AND KNOWLEDGE-BASED EXPERT SYSTEMS TO CREATE A HYBRID REMOTE SENSING SYSTEM THAT CAN OUTPERFORM EITHER METHOD ALONE. IN OUR STUDY, WE PROPOSE TO DEVELOP AN ADVANCED REMOTE SENSING SYSTEM, WITHIN A HYBRID NEURAL NETWORK/EXPERT SYSTEMENVIRONMENT, TO BE INCORPORATED IN THE GEOSTATIONARY EARTH OBSERVATORY. THE SYSTEM WILL TAKE ADVANTAGE OF BOTH ANNS AND EXPERT SYSTEMS TO HANDLE GLOBAL AND LOCAL EVENT ISOLATION AND IDENTIFICATION, MEASUREMENT VALIDATION, INSTRUMENTATION CONTROL AND INFORMATION STORAGE. THE HYBRIDREMOTE SENSING SYSTEM WILL BE IMPLEMENTED WITHIN THE HYBRID NUEX SHELL, PROVIDING A VISUAL OBJECT ORIENTED DEVELOPMENT ENVIRONMENT FOR REAL-TIME REMOTE SENSING APPLICATIONS.

* Information listed above is at the time of submission. *

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