Connectionist Models for Intelligent Computation.

Abstract

This final report covers the work done by our group of neural network computing at the University of Maryland for the past three years. We studied the neural network's capability of processing temporal or sequential data. Recurrent neural networks were used to perform inference cn grammers. An external memory stack was constructed to work with the neural network to perform inferences on context free languages. And finally, a spatially homogeneous locally connected recurrent neural network that could simulate any given turing machine, including the universal Turing machine was devised. It is capable of performing universal computations and demonstrated the universal power of recurrent neural network architectures. To train these sequential neural net machine, we have investigated the forward propagating learning algorithms. (KAR) P. 1

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Document Details

Document Type
Technical Report
Publication Date
Jul 28, 1994
Accession Number
ADA296789

Entities

People

  • H. H. Chen
  • Y. C. Lee

Organizations

  • University of Maryland

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Automata
  • Cognitive Science
  • Computational Science
  • Computations
  • Grammars
  • Information Systems
  • Language
  • Learning
  • Machines
  • Network Architecture
  • Neural Networks
  • Recurrent Neural Networks
  • Signal Processing
  • Simulations

Fields of Study

  • Computer science

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Artificial Intelligence
  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks