Signal Approximation with a Wavelet Neural Network

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.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1992
Accession Number
ADA259081

Entities

People

  • Charles M. Westphal

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Electronic Warfare
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Electrical Engineering
  • Engineering
  • Fourier Analysis
  • Frequency
  • Frequency Domain
  • Identification
  • Integrals
  • Kernel Functions
  • Machine Learning
  • Network Architecture
  • Neural Networks
  • Signal Processing
  • Standards
  • Time Domain
  • Wavelet Transforms

Readers

  • Approximation Theory.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Microelectronics