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
ADA343441

Entities

People

  • Eduardo D. Sontag
  • Hector Sussmann

Organizations

  • Rutgers University–New Brunswick

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Aircrafts
  • Artificial Intelligence
  • Automata
  • Automata Theory
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Computer Science
  • Computers
  • Content Addressable Memory
  • Control Systems Engineering
  • Differential Equations
  • Information Science
  • Linear Systems
  • Machine Learning
  • Neural Networks
  • Nonlinear Dynamics

Readers

  • Approximation Theory.
  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks