Topics in Stochastics, Symbolic Dynamics and Neural Networks

Abstract

Research was supported in inverse application areas of probability, ergodic theory and dynamical systems (including neural networks). Theorems on rates of learning in unsupervised Neural Networks, relating to the sampling method for available environmental data were obtained. Results on the consistency and effectiveness of estimators for correlation dimension were derived, together with advanced percolation structures useful in mammalian lung development models. Ways of using 'continued fractions' to construct highly mixing stochastic processes were expounded.

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Document Details

Document Type
Technical Report
Publication Date
Dec 31, 1996
Accession Number
ADA336426

Entities

People

  • Robert M. Burton Jr

Organizations

  • Oregon State University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Consistency
  • Data Science
  • Dynamics
  • Estimators
  • Geometry
  • Learning
  • Mathematics
  • Mechanics
  • Mixing
  • Neural Networks
  • Numbers
  • Percolation
  • Probability
  • Real Numbers
  • Stochastic Processes
  • Word Processors

Readers

  • Mathematical Modeling and Probability Theory.
  • Oncology and Biomarker-Based Cancer Detection.
  • Theoretical Analysis.

Technology Areas

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