Johnson Distributions for Fitting Weighted Sums of Sigmoided Neuron Outputs

Abstract

In this paper, it is shown that a continuum of distributions best characterizes the hidden layer outputs of a multilayer perceptron when trained as a 0-1 classifier and tested with a range of signal-to-noise ratio (SNR) input distributions. A four parameter system of transformed normal distributions, known as the Johnson system of distributions, is utilized to illustrate the shape of output distributions as a function of input SNR levels. Neural networks, Active signal processing.

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Document Details

Document Type
Technical Report
Publication Date
Jul 11, 1993
Accession Number
ADA273259

Entities

People

  • J. T. Durham
  • W. C. Torrez

Organizations

  • Naval Command, Control and Ocean Surveillance Center

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Abstracts
  • Classification
  • Distribution Functions
  • Information Processing
  • Information Science
  • Machine Learning
  • Naval Warfare
  • Neural Networks
  • Normal Distribution
  • Ocean Surveillance
  • Optimization
  • Probability
  • Probability Density Functions
  • Recognition
  • Skewness
  • Standards

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks