Multi-model interpolation of range-varying acoustic propagation

Author

Chin, Daniel C. ; Biomdo, A.C.

Author_Institution

Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA

Volume

5

fYear

2000

fDate

2000

Firstpage

3008

Abstract

This paper develops a model-fitting technique to perform the interpolation and extrapolation of a nonlinear time-varying system. The development is demonstrated on the problem of transmission loss of underwater sound. The technique involves simplified time-varying multiple models, neural networks (NNs), and multiobjective simultaneous perturbation stochastic approximation (MSPSA). The simplified models represent the local phenomena that change in time, the NNs capture the model variations, and MSPSA trains the NN-weights. The localized multi-model technique has shown accuracy and efficiency in the transmission loss interpolation

Keywords

approximation theory; curve fitting; extrapolation; interpolation; neural nets; nonlinear systems; physics computing; time-varying systems; underwater acoustic propagation; acoustic propagation; extrapolation; interpolation; model-fitting; neural networks; nonlinear system; perturbation stochastic approximation; time-varying system; underwater sound; Acoustic propagation; Acoustic reflection; Extrapolation; Interpolation; Laboratories; Neural networks; Physics; Propagation losses; Stochastic processes; Time varying systems;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2000. Proceedings of the 2000

Conference_Location

Chicago, IL

ISSN

0743-1619

Print_ISBN

0-7803-5519-9

Type

conf

DOI

10.1109/ACC.2000.879117

Filename

879117