Mathematical Theory of Neural Networks

Abstract

The work pursued under this grant dealt with artificial neural networks and other discrete/continuous models. New bounds were obtained for sample complexity for identification of static and dynamic concept classes defined by static and recurrent networks. Structural and system-theoretic properties were characterized, leading to effective tests for identifiability and other properties. Related models of hybrid systems were also studied; an equivalence problem for PL systems was shown to be decidable in polynomial time, and a general Maximum Principle was established for hybrid systems.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2000
Accession Number
ADA387942

Entities

People

  • Eduardo D. Sontag
  • Hector J. Sussman

Organizations

  • Rutgers University–New Brunswick

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Computer Programming
  • Computer Science
  • Computers
  • Content Addressable Memory
  • Control Systems
  • Hybrid Systems
  • Identification
  • Information Science
  • Information Systems
  • Linear Systems
  • Mathematics
  • Neural Networks
  • Polynomials
  • Recurrent Neural Networks

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.

Technology Areas

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