Radar System Classification Using Neural Networks

Abstract

This study investigated methods of improving the accuracy of neural networks in the classification of large numbers of classes. A literature search revealed that neural networks have been successful in the radar classification problem, and that many complex problems have been solved using systems of multiple neural networks. The experiments conducted were based on 32 classes of radar system data. The neural networks were modelled using a program called the Neural Graphics Analysis System. It was found that the accuracy of the individual neural networks could be increased by controlling the number of hidden nodes, the relative numbers of training vectors per class, and the number of training iterations. The maximum classification accuracy of 96.5% was achieved using a hierarchy of neural networks in which the classes were partitioned based on their performances in a large neural network trained with all classes.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1991
Accession Number
ADA243631

Entities

People

  • David M. Cameron

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Electronic Warfare

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Artificial Intelligence
  • Case Studies
  • Classification
  • Computer Programming
  • Data Sets
  • Experimental Design
  • Information Science
  • Kernel Functions
  • Machine Learning
  • Neural Networks
  • Radar
  • Radar Signals
  • Recognition
  • Standards
  • Statistical Analysis

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Operations Research

Technology Areas

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