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A SPATIO-TEMPORAL BACKPROPAGATION NEURAL NETWORK APPROACH TOFISHERY STOCK…

Award Information

Agency:
Department of Commerce
Branch:
N/A
Award ID:
17464
Program Year/Program:
1992 / SBIR
Agency Tracking Number:
17464
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
LNK CORP., INC.
6811 Kenilworth Avenue, Suite 306 Riverdale, MD 20737
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Woman-Owned: No
Minority-Owned: Yes
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 1992
Title: A SPATIO-TEMPORAL BACKPROPAGATION NEURAL NETWORK APPROACH TOFISHERY STOCK PREDICTION
Agency: DOC
Contract: N/A
Award Amount: $34,775.00
 

Abstract:

THE PRIMARY OBJECTIVE OF THIS PROPOSAL IS TO DESIGN AND BUILD A SYSTEM FOR FISHERY AND MARINE MAMMAL STOCK PREDICTION USING NONLINEAR SPATIO-TEMPORAL NEURAL NETWORK THEORY. WHILE THE CENTRAL ENGINE OF THE SYSTEM MAKES USE OFNONLINEAR NEURAL NETWORKS FOR PREDICTION, SEVERAL PERIPHERAL MODULES INCORPORATED IN THE DESIGN ASSIST THE USER IN EMPLOYING THE PREDICTOR FOR VARIOUS APPLICATIONS. IN PARTICULAR, OUR APPROACH INVOLVES A USER-FRIENDLY EXPERT SYSTEM AT THE FRONT END TO INTERACT WITH THE USER ON THE DATA DOMAIN, WHILE A RECONFIGURABLE NONLINEAR NEURAL NETWORKFORMS THE CRUX OF A PROBLEM SOLVING SYSTEM. NEURAL NETWORKSHAVE PROVENLY OUTPERFORMED TRADITIONAL LINEAR AND OTHER STATISTICAL PREDICTORS IN THE PAST FOR VARIOUS APPLICATIONS,INCLUDING ENERGY DEMAND PREDICTION (WERBOS 1988). ONE OF THE INHERENT DIFFICULTIES WITH THEM IS THEIR INABILITY TO "EXPLAIN" TO THE USER THEIR DECISIONS. OUR CHOICE OF EXPERTSYSTEMS TO INTERACT WITH THE USER IS WARRANTED BECAUSE OF THE USER-FRIENDLY NATURE OF NATURAL LANGUAGE TYPE INTERACTIONS THAT USERS ARE COMFORTABLE WITH. HENCE AN EXPERT-NEURAL NETWORK PREDICTOR.

Principal Investigator:

Dr. Srinivasan Raghavan
0

Business Contact:

Small Business Information at Submission:

Lnk Corpon Inc
6811 Kenilworth Ave Suite 306 Riverdale, MD 20737

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