AN ANALOG LINEAR CLASSIFICATION NETWORK.

Abstract

A linear network was built to classify analog signals consisting of a large number of parallel inputs. These inputs were derived by a feature abstracting system using synthetic nerve networks. In this classification network the signals pass simultaneously through a maximum amplitude filter and then are classified by a resistive memory matrix. The maximum amplitude filter attenuates smaller inputs much more than larger ones and serves to 'pick out' predominant features. This is a way of utilizing the pandemonium concept of Selfridge. Classification by linear hyperplanes is discussed briefly and then the operation and design of the maximum amplitude filter is covered. (Author)

Document Details

Document Type
Technical Report
Publication Date
Apr 27, 1966
Accession Number
AD0641178

Entities

People

  • Robert J. Biegalski

Organizations

  • Naval Ordnance Laboratory

Tags

DTIC Thesaurus Topics

  • Amplitude
  • Analog Signals

Fields of Study

  • Engineering

Readers

  • Approximation Theory.
  • Control Systems Engineering.
  • Neural Network Machine Learning.