INFORMATION THEORY, LEARNING SYSTEMS, AND SELF-ORGANIZING CONTROL SYSTEMS

Abstract

Learning systems are defined on three levels of complexity, the trained system, the adaptive system, and the self-organizing system. The functional purpose of each is discussed in terms of the removal of noise and a theorem stating a necessary condition for adaption is stated and proven. The theorem is then applied to a simple form of adaptive system, and it is shown that for a linear threshold device employed within its 'natural capacity', two bits per weight are necessary. The information theoretic model of the self- organizing system is translated into a goal directed control system model. This self-organizing control system model is analyzed and shown to have a simple performance surface that can be searched by relaxation methods. Experiments are discussed.

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 1967
Accession Number
AD0656082

Entities

People

  • Malcolm Rucj Uffelman

Tags

Communities of Interest

  • Air Platforms
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Adaptive Systems
  • Analog Computers
  • Assistive Technologies
  • Control Systems
  • Information Science
  • Information Theory
  • Learning
  • Learning Machines
  • New York
  • Pattern Recognition
  • Probability
  • Recognition
  • Self Organizing Systems
  • Training
  • Transfer Functions
  • United States

Readers

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  • Systems Analysis and Design