Genetic algorithm wavelet design for signal classification

Author

Jones, Eric ; Runkle, Paul ; Dasgupta, Nilanjan ; Couchman, Luise ; Carin, Lawrence

Author_Institution

Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA

Volume

23

Issue

8

fYear

2001

fDate

8/1/2001 12:00:00 AM

Firstpage

890

Lastpage

895

Abstract

Biorthogonal wavelets are applied to parse multiaspect transient scattering data in the context of signal classification. A language-based genetic algorithm is used to design wavelet filters that enhance classification performance. The biorthogonal wavelets are implemented via the lifting procedure and the optimization is carried out using a classification-based cost function. Example results are presented for target classification using measured scattering data

Keywords

filtering theory; genetic algorithms; signal classification; wavelet transforms; GA; biorthogonal wavelets; classification-based cost function; genetic algorithm wavelet design; language-based genetic algorithm; lifting procedure; measured scattering data; multiaspect transient scattering data parsing; optimization; signal classification; target classification; wavelet filter design; Algorithm design and analysis; Cost function; Discrete wavelet transforms; Filter bank; Finite impulse response filter; Genetic algorithms; Pattern classification; Scattering; Signal design; Wavelet analysis;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.946991

Filename

946991