Neural network adaptive wavelets for signal representation and classification

Harold H. Szu; Brian A. Telfer; Shubha L. Kadambe

doi:10.1117/12.59918

1 September 1992 Neural network adaptive wavelets for signal representation and classification

Harold H. Szu, Brian A. Telfer, Shubha L. Kadambe

Author Affiliations +

Optical Engineering, Vol. 31, Issue 9, (September 1992). https://doi.org/10.1117/12.59918

Abstract

Methods are presented for adaptively generating wavelet templates for signal representation and classification using neural networks. Different network structures and energy functions are necessary and are given for representation and classification. The idea is introduced of a "super-wavelet," a linear combination of wavelets that itself is treated as a wavelet. The super-wavelet allows the shape of the wavelet to adapt to a particular problem, which goes beyond adapting parameters of a fixed-shape wavelet. Simulations are given for 1-D signals, with the concepts extendable to imagery. Ideas are discussed for applying the concepts in the paper to phoneme and speaker recognition.

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Harold H. Szu, Brian A. Telfer, and Shubha L. Kadambe "Neural network adaptive wavelets for signal representation and classification," Optical Engineering 31(9), (1 September 1992). https://doi.org/10.1117/12.59918

Published: 1 September 1992

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