Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks

Abstract

The relationship between 'learning' in adaptive layered networks and the fitting of data with high dimensional surfaces is discussed. This leads naturally to a picture of 'generalization in terms of interpolation between known data points and suggests a rational approach to the theory of such networks. A class of adaptive networks is identified which makes the interpolation scheme explicit. This class has the property that learning is equivalent to the solution of a set of linear equations. These networks thus represent nonlinear relationships while having a guaranteed learning rule. Great Britain.

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Document Details

Document Type
Technical Report
Publication Date
Mar 28, 1988
Accession Number
ADA196234

Entities

People

  • D. S. Broomhead
  • David Lowe

Organizations

  • Royal Signals and Radar Establishment

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Artificial Intelligence
  • Automated Speech Recognition
  • Chebyshev Polynomials
  • Curve Fitting
  • Data Science
  • Eigenvalues
  • Equations
  • Hidden Markov Models
  • Interpolation
  • Markov Models
  • Network Science
  • Numerical Analysis
  • Statistics
  • Stochastic Processes
  • Training

Fields of Study

  • Computer science

Readers

  • Calculus or Mathematical Analysis
  • Neural Network Machine Learning.
  • Theoretical Analysis.