Surviving the Data Deluge: A Combined Dynamical Systems/Machine Learning Approach

Abstract

This research sought to develop a comprehensive, computationally tractable framework for synthesizing information driven systems capable of both autonomously operating and supporting safety--critical human operations in rapidly changing ``data deluged' scenarios. Its conceptual backbone was a rigorous integration of systems theory, machine learning and optimization elements that emphasized robustness, computational simplicity and improved situational awareness. The research advanced the state of the art in systems theory by developing a tractable framework for robust identification/model (invalidation) of a broad class of dynamical systems that incorporates ideas from machine learning and semi-algebraic optimization to handle outliers, missing data and substantial noise levels.

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Document Details

Document Type
Technical Report
Publication Date
Jun 25, 2020
Accession Number
AD1104379

Entities

People

  • Mario Sznaier

Organizations

  • Northeastern University

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Aircrafts
  • Autonomous Systems
  • Computational Complexity
  • Computer Vision
  • Computers
  • Control Systems
  • Data Set
  • Data Sets
  • Detection
  • Detectors
  • Digital Data
  • Dimensionality Reduction
  • Equations
  • Filters
  • First Responders
  • Geometry
  • Information Science
  • Machine Learning
  • Multiple Input Multiple Output
  • Systems Engineering
  • Unmanned Aerial Vehicles
  • Unmanned Vehicles

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks