MULTISENSOR INFORMATION PROCESSING USING A MULTIPLE NEURAL NETWORK LEARNING SYSTEM

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$49,888.00
Award Year:
1988
Program:
SBIR
Phase:
Phase I
Contract:
n/a
Award Id:
8905
Agency Tracking Number:
8905
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
1 Richmond Sq, Providence, RI, 02906
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
DR DOUGLAS L REILLY
(401) 331-9640
Business Contact:
() -
Research Institution:
n/a
Abstract
THE OBJECTIVE IS TO DEMONSTRATE THE FEASIBILITY OF USING NESTOR'S NEURAL NETWORK LEARNING TECHNOLOGY FOR PATTERN RECOGNITION IN A MULTISENSOR AVIONICS SYSTEM ENVIRONMENT. PATTERN MATCHING VIA PARALLEL SEARCHES IN A MULTISENSOR DATA ENVIRONMENT MUST BE CONTROLLED BY AN APPROPRIATE HIERARCHICAL DECISION LOGIC THROUGH WHICH A SYSTEM CAN LEARN HOW BEST TO USE ITS AVAILABLE DATA FOR ROBUST RECOGNITION. NESTOR HAS PIONEERED A NOVEL SYSTEMS APPROACH TO USING MULTIPLE NEURAL NETWORKS FOR PATTERN RECOGNITION, WHERE EACH COMPONENT NETWORK OF THE SYSTEM CAN BE CONFIGURED TO PROCESS INFORMATION FROM A SINGLE SENSOR OR MULTIPLE SENSORS. A HIGH LEVEL CONTROLLER, OPERATING ON THE SYMBOLIC OUTPUT OF INDIVIDUAL NETWORKS, INTEGRATES THEIR RESPONSES AND DETERMINES FEEDBACK TRAINING SIGNALS. THIS TECHNOLOGY HAS BEEN SUCCESSFULLY APPLIED TO PATTERN RECOGNITION PROBLEMS IN CHARACTER RECOGNITION, VISUAL OBJECT RECOGNITION AND THE SIMULATION OF HUMAN EXPERT DECISION MAKING. WE PROPOSE TO ASSESS THE PERFORMANCE OF THE SYSTEM ON REAL-WORLD MULTISENSOR DATA TO SHOW THE FEASIBILITY OF USING THE TRAINABLE DECISION-MAKING LOGIC OF THE SYSTEM FOR MULTISENSOR PROCESSION IN AN AVIONICS ENVIRONMENT.

* information listed above is at the time of submission.

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