Distributed Consensus Learning and Approximation for Geometric and Abstract Surfaces

Abstract

The ARO grant entitled "Distributed Consensus Learning for Geometric and Abstract Surfaces " (ARO Grant #W911NF-13-1-0407) considers abstract surface approximation using distributed, sparse, or scattered observations obtained from a large class of sensors that are used by decentralized sensor vehicle networks. The overall goals of the research program can be organized in three major objectives. (1) One goal of this program of research has been to study and derive rates of convergence for (discrete time) consensus function estimates obtained from collectives of multiple learning agents. One topic under this objective is to study rates of convergence in appropriate in finite dimensional approximation or smoothness spaces. This goal should be contrasted with the large body of literature that derives rates of convergence in time for states that evolve in some fixed, low dimensional spaces Rd.

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Document Details

Document Type
Technical Report
Publication Date
Mar 28, 2019
Accession Number
AD1080619

Entities

People

  • Andrew J. Kurdila

Organizations

  • Virginia Tech

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Computational Science
  • Detectors
  • Differential Equations
  • Distance Learning
  • Eigenvalues
  • Equations
  • Estimators
  • Geometry
  • Hilbert Space
  • Information Exchange
  • Markov Chains
  • Multiagent Systems
  • Nonlinear Systems
  • Probability
  • Sensor Networks
  • Simultaneous Localization And Mapping
  • Software Development
  • Standards
  • Stochastic Processes
  • Theses
  • Three Dimensional
  • Topology
  • Websites

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Approximation Theory.
  • Research Science/Academic Research

Technology Areas

  • Space