A Comparison of Nonlinear Optimisation Strategies for Feed-Forward Adaptive Layered Networks

Abstract

This work discusses various learning strategies which may be employed for the generic class of layered feed-forward adaptive networks exemplified by the traditional Multilayer Perceptron. Such a network is only useful if a set of weight values exists which allows the network to form a good approximation to an underlying (and possibly unknown) transformation between input and output patterns.

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Document Details

Document Type
Technical Report
Publication Date
Jul 05, 1988
Accession Number
ADA200044

Entities

People

  • A. R. Webb
  • David Lowe
  • M. D. Bedworth

Organizations

  • Royal Signals and Radar Establishment

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Automated Speech Recognition
  • Computational Science
  • Computations
  • Computer Programs
  • Data Analysis
  • Far Field
  • Focal Plane Arrays
  • Focal Planes
  • Frequency
  • Geometry
  • Pattern Recognition
  • Probability
  • Statistics
  • Test Sets
  • Transfer Functions

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design