Cognitively Inspired Neural Network for Situation Recognition

Abstract

We developed a cognitively inspired mathematical learning framework called Neural Modeling Fields (NMF). We applied it to learning and to recognition of situation composed of objects. NMF can successfully overcome the combinatorial complexity of associating subsets of objects with situations and provides fast and reliable convergence. We discuss the implications of the results of this current work for building multi layered intelligent systems.

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Document Details

Document Type
Technical Report
Publication Date
Jan 14, 2010
Accession Number
ADA525506

Entities

People

  • Leonid Perlovsky
  • Roman Ilin

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Cognition
  • Computer Languages
  • Computer Vision
  • Information Processing
  • Information Science
  • Intelligent Agents
  • Intelligent Systems
  • Machine Learning
  • Neural Networks
  • Object Recognition
  • Psychology
  • Recognition
  • Situational Awareness

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

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