Automatic Data Sorting Using Neural Network Techniques.

Abstract

When several data sources are sending asynchronously without any multiplexing conventions, the stream of data from each source will be interleaved in an unpredictable sequence. In such a sequence, it would be highly desirable to deinterleave the data streams before attempting further processing. After the application of certain signal processing techniques on the incoming interleaved data stream, one obtains a feature space in which different data sources typically form distinct clusters. It is therefore essential to have a reliable clustering technique to determine: (1) the correct number of sources, and (2) the correct membership for each datum. The Hopfield-Kamgar neural net clustering technique appears to be the clustering technique of choice for this task. We explain the main aspects of our technique and briefly discuss alternative neural nets and conventional methods for clustering, in particular as applied to data deinterleaving.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1996
Accession Number
ADA304447

Entities

People

  • Behrooz Kamgar-Parsi
  • Behzad Kamgar-Parsi
  • John C. Sciortino Jr.

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Electronic Warfare
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Automatic
  • Clustering
  • Data Analysis
  • Data Science
  • Data Sets
  • Differential Equations
  • Firing Rate
  • Gaussian Distributions
  • Information Science
  • Information Systems
  • Neural Networks
  • Signal Processing
  • Standards
  • Two Dimensional

Readers

  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Space