Computation and Generalization in Neural Networks

Abstract

During this contract period, our research into backward propagation has led to a number of new theoretical and empirical results. We have developed a generalized version of backward propagation. In our generalized network, both gains and synapses are modified by a backward propagation procedure. Synapses are modified in proportion to the negative gradient of the energy with respect to the synaptic weight as in ordinary backward propagation, and gains are modified in proportion to the negative partial derivative with respect to gain. Since the resulting error signals for the gain and synaptic weights are proportional to one another, the computational complexity of our generalized network is comparable to that of the original backward propagation model.... Back propagation, Gain modification, Multilayer perceptrons.

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Document Details

Document Type
Technical Report
Publication Date
May 05, 1993
Accession Number
ADA263752

Entities

People

  • Leon Cooper

Organizations

  • Brown University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Boundaries
  • Classification
  • Computational Complexity
  • Computations
  • Convergence
  • Four Dimensional
  • Information Operations
  • Mathematics
  • Military Research
  • Momentum
  • Neural Networks
  • Rhode Island
  • Simulations
  • Training
  • Two Dimensional
  • Vector Spaces

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Statistical inference.
  • Wave Propagation and Nonlinear Chaotic Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks