Parallel Implementation of an Artificial Neural Network Integrated Feature and Architecture Selection Algorithm

Abstract

The selection of salient features and an appropriate hidden layer architecture contributes significantly to the performance of a neural network. A number of metrics and methodologies exist for estimating these parameters. This research builds on recent efforts to integrate feature and architecture selection for the multilayer perceptron. In the first stage of work a current algorithm is developed in a parallel environment, significantly improving its efficiency and utility. In the second stage, improvements to the algorithm are proposed. With regards to feature selection, a common random number (CRN) addition is proposed. Two new methods of architecture selection are examined, to include an information criterion and a signal to noise based procedure. These methodologies are shown to improve algorithm performance.

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Document Details

Document Type
Technical Report
Publication Date
Mar 13, 1998
Accession Number
ADA342706

Entities

People

  • Craig W. Rizzo

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Central Processing Units
  • Computer Programming
  • Computers
  • Dimensionality Reduction
  • Environment
  • Evolutionary Algorithms
  • Feature Selection
  • Instructions
  • Neural Networks
  • Operations Research
  • Parallel Computing
  • Recurrent Neural Networks
  • Shell Scripts
  • Standards
  • Test Sets

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks