Stability and Adaptation of Neural Networks

Abstract

Focused on unsupervised learning and adaptive fuzzy systems. Explored the differential competitive learning (DCL) law. We successfully benchmarked DCL against supervised competitive learning for phoneme recognition and centroid estimation. Proved structural stability for general competitive learning laws. Developed product-space clustering to develop adaptive fuzzy systems, which grow structured fuzzy rules from training data without supervision. Successfully benchmarked adaptive fuzzy systems against neural-network truck-and-trailer systems and Kalman-filter control systems for realtime target tracking.

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Document Details

Document Type
Technical Report
Publication Date
Nov 02, 1990
Accession Number
ADA230108

Entities

People

  • Bart Kosko

Organizations

  • University of Southern California

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Bayesian Networks
  • Cognitive Science
  • Computational Science
  • Control Systems
  • Data Science
  • Differential Equations
  • Electrical Engineering
  • Information Science
  • Kalman Filters
  • Machine Learning
  • Mathematical Filters
  • Network Science
  • Neural Networks
  • Pattern Recognition
  • Self Organizing Systems

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.

Technology Areas

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