Mathematical Theory of Neural Networks

Abstract

This report focuses on fundamental theoretical issues relevant to the capabilities, performance, and limitations of artificial neural networks. For static (feedforward) networks, subjects of investigation included the study of error surfaces for least squares fitting, VC and other learning dimensions, representability questions, and function approximation. For dynamic (recurrent) nets, covered are questions dealing with parameter identification and modeling, realizability and other systems-theoretic issues, theoretical computational capabilities, and learning-theoretic issues.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1997
Accession Number
ADA332338

Entities

People

  • Eduardo D. Sontag
  • Hector J. Sussmann

Organizations

  • Rutgers University–New Brunswick

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Analytic Functions
  • Artificial Intelligence
  • Automata
  • Automata Theory
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Computer Science
  • Computers
  • Content Addressable Memory
  • Differential Equations
  • Linear Systems
  • Machine Learning
  • Neural Networks
  • Numerical Analysis

Readers

  • Control Systems Engineering.
  • Neural Network Machine Learning.
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks