Hybrid Neural Network for Pattern Recognition.

Abstract

A system for recognizing patterns comprises a first stage for extracting features from inputted patterns and for providing topological representations of the characteristics of the inputted patterns and a second stage for classifying and recognizing the inputted patterns. The first stage comprises two one-layer neural networks and the second stage comprises a feedforward two-layer neural network. A method for recognizing patterns is also described, which method broadly comprises the steps of providing first and second neural networks, each having an input layer formed by a plurality of input neurons and an output layer formed by a plurality of output neurons, supplying signals representative of a set of inputted patterns to the input layers of the first and second neural networks, training the first and second neural networks using a competitive learning algorithm, and generating topological representations of the input patterns using the first and second neural networks. The method further comprises providing a third neural network for classifying and recognizing the inputted patterns and training the third neural network with a back-propagation algorithm so that the third neural network recognizes at least one interested pattern.

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Document Details

Document Type
Technical Report
Publication Date
Feb 03, 1997
Accession Number
ADD018425

Entities

People

  • Chung T. Nguyen

Organizations

  • United States Department of the Navy

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computing System Architectures
  • Data Acquisition
  • Detectors
  • Feature Extraction
  • Inventions
  • Learning
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Recognition
  • Self Organizing Systems
  • Signal Processing
  • Training

Fields of Study

  • Computer science

Readers

  • Electrical Engineering
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks