Signal Approximation with a Wavelet Neural Network

Author

Charles M. Westphal

Date of Award

12-1992

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Department of Electrical and Computer Engineering

Abstract

This study investigated the use of Wavelet Neural Networks (WNN) for signal approximation. The particular wavelet function used in this analysis consisted of a summation of sigmoidal functions (a sigmoidal wavelet). The sigmoidal wavelet has the advantage of being easily implemented in hardware via specialized electronic devices like the Intel Electronically Trainable Analog Neural Network (ETANN) chip. The WNN representation allows the determination of the number of hidden-layer nodes required to achieve a desired level of approximation accuracy. Results show that a bandlimited signal can be accurately approximated with a WNN trained with irregularly sampled data. Signal approximation, Wavelet neural network.

AFIT Designator

AFIT-GE-ENG-92D-38

DTIC Accession Number

ADA259081

Comments

The author's Vita page is omitted.

Recommended Citation

Westphal, Charles M., "Signal Approximation with a Wavelet Neural Network" (1992). Theses and Dissertations. 7148.
https://scholar.afit.edu/etd/7148