A polynomial network for predicting temperature distributions

Author

Fulcher, George E. ; Brown, Donald E.

Author_Institution

Inst. for Parallel Comput., Virginia Univ., Charlottesville, VA, USA

Volume

5

Issue

3

fYear

1994

fDate

5/1/1994 12:00:00 AM

Firstpage

372

Lastpage

379

Abstract

Complete temperature distributions are unavailable for many locations throughout the world. This distributional information is important for product design and operational planning. The problem of obtaining these temperature distributions is quite difficult and current techniques are limited in accuracy. This paper describes a new and effective approach to this problem that matches data-deficient locations to maximally similar locations with known distributions. A polynomial network is used to predict which of a set of sites with known distributions is most similar to a data-deficient site. Then an information theoretic criterion is optimized to find the unknown distribution that closely matches this maximally similar site. Tests with this approach demonstrate its effectiveness and its superiority to current methods

Keywords

atmospheric temperature; geophysics computing; meteorology; neural nets; weather forecasting; data-deficient locations; geophysics; information theoretic criterion; maximally similar locations; polynomial neural network; temperature distributions prediction; weather; Concurrent computing; Costs; Databases; National security; Polynomials; Product design; Systems engineering and theory; Temperature distribution; Temperature sensors; Testing;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.286909

Filename

286909