Zum Lernverhalten von Backpropagation-Netzen auf der Basis stochastischer Rechentechnik (On the Learning Process of Back-Propagation Nets on the Basis of Stochastic Computation)

Abstract

The dissertation entails a detailed examination of the adaptive or learning process of a back-propagation net, used for the building of massive parallel neural networks, and based on the level of mathematical induction, stochastic computation, and software simulation. Firstly are the introductory bases of the back-propagation new discussed. Parameters and an adaptive process are then selected, followed by limitations and countermeasures. The results of the examination and software simulation demonstrate the simultaneously occurring work and learning processes do not seem to exert any negative influence on the convergence progress. The more precise and quantitative scalability of the entire process is evaluated in the study's final chapter.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2000
Accession Number
ADA407931

Entities

People

  • Liyun Zhu

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Character Recognition
  • Computations
  • Computers
  • Dimensionality Reduction
  • Electrical Engineering
  • Learning
  • Machine Learning
  • Network Architecture
  • Neural Networks
  • Recognition
  • Signal Processing
  • Simulations
  • Simulators
  • Theses
  • Very Large Scale Integration

Readers

  • Artificial Intelligence
  • Computational Fluid Dynamics (CFD)
  • Theoretical Analysis.

Technology Areas

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