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NONLINEAR FORECASTING TECHNIQUES

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: N/A
Agency Tracking Number: 11797
Amount: $49,900.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 1990
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
15 Washington Court
East Windsor, NJ 08620
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 David J Anderson
 President
 (609) 448-8622
Business Contact
Phone: () -
Research Institution
N/A
Abstract

THE APPLICATION OF HYBRID NEURAL NETWORK ARCHITECTURES CAN HAVE ADVANTAGES OVER STOCHASTIC TIME SERIES ANALYSIS TECHNIQUES AND OTHER REGRESSION TECHNIQUES IN FORECASTING. THESE NONLINEAR SYSTEMS CAN EXHIBIT THE ABILITY TO REPRESENTNONLINEAR MAPPING NETWORKS, TO MANAGE MULTIVARIATE DATA, TO MANAGE TEMPORAL RELATIONSHIPS, AND TO FUNCTION WITHOUT THE PRIOR SELECTION OF A BASIS FUNCTION. THIS PHASE I PROGRAM WILL IDENTIFY AND CHARACTERIZE POTENTIAL HYBRID NEURAL NETWORK ARCHITECTURES WHICH PROVIDE SIGNIFICANT PERFORMANCE ADVANTAGES OVER PRESENTLY USED TECHNIQUES IN FORECASTING LARGE SCALE NONLINEAR SYSTEMS. IN ADDITION, THIS PROGRAM WILL COMPARE THE PERFORMANCE OF CURRENTLY USED TIME SERIES TECHNIQUES WITH THE PERFORMANCE OF SPECIALIZED NEURAL NETWORK ARCHITECTURES. THIS PROJECT HAS STRONG COMMERCIAL APPLICATIONS. NONLINEAR FORECASTING SYSTEMS ARE PARTICULARLY WELL SUITED FOR ELECTRIC POWER FORECASTING, ECONOMETRIC MODELLING, AND TRANSPORTATION FORECASTING. WITH THE ADDITION OF A POWERFULUSER INTERFACE TO THE OPTIMIZED NONLINEAR SYSTEMS OF PHASE II, THE FINAL SYSTEM IS EXPECTED TO BE VERY USEFUL TO THE ABOVE MENTIONED INDUSTRIES. THEREFORE, FOLLOW-ON FUNDING ISFULLY EXPECTED.

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

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