An Investigation of the Application of Artificial Neural Networks to Adaptive Optics Imaging Systems

Abstract

Recurrent and feedforward artificial neural networks are developed as wavefront reconstructors. The recurrent neural network studied is the Hopfield neural network and the feedforward neural network studied is the single layer perceptron artificial neural network. The recurrent artificial neural network input features are the wavefront sensor slope outputs and neighboring actuator feedback commands. The feedforward artificial neural network input features are just the wavefront sensor slope outputs. Both artificial neural networks use their inputs to calculate deformable mirror actuator commands. The effects of training are examined.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1991
Accession Number
ADA243780

Entities

People

  • Andrew H. Suzuki

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Adaptive Optics
  • Algorithms
  • Atmospheric Motion
  • Computers
  • Content Addressable Memory
  • Control Systems
  • Deformable Mirrors
  • Detectors
  • Electrical Engineering
  • Equations
  • Estimators
  • Geometry
  • Ground Based
  • Information Science
  • Neural Networks
  • Probability Distributions
  • Recurrent Neural Networks

Fields of Study

  • Physics

Readers

  • Neural Network Machine Learning.
  • Optical Physics and Photonics.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks