Data Analysis of Complex Systems

Abstract

The goal of this research effort is to investigate methods to fuse vast amounts of data coming from different sensor sources with a multi-layered semi-supervised learning approach. This approach will use basic statistical techniques to identify key predictors, some correlation techniques to validate the source, quality and temporal aspects of the data, artificial neural networks for troubleshooting sources of system variability, and semi-supervised learning techniques which will provide adjustable thresholds for forecasting and detecting various anomalies or events of interest.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2011
Accession Number
ADA546141

Entities

People

  • Misty K. Blowers

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Application Software
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Complex Systems
  • Computational Science
  • Computer Programs
  • Control Systems
  • Data Mining
  • Data Science
  • Databases
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Network Science
  • Neural Networks
  • Supervised Machine Learning

Readers

  • Artificial Intelligence
  • Computational Modeling and Simulation
  • Distributed Systems and Data Platform Development

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks