A Neural Network Implementation of Chaotic Time Series Prediction

Abstract

This thesis provides a description of how a neural network can be trained to learn the order inherent in chaotic time series data and then use that knowledge to predict future time series values. It examines the meaning of chaotic time series data, and explores in detail the Glass-Mackey nonlinear differential delay equation as a typical source of such data. An efficient weight update algorithm is derived, and its two-dimensional performance is examined graphically. A predictor network which incorporates this algorithm is constructed and used to predict chaotic data. The network was able to predict chaotic data. Prediction was more accurate for data having a low fractal dimension than for high-dimensional data. Lengthy computer run times than for high-dimensional data. Lengthy computer run times were found essential for adequate network training. Keywords: Sine waves, Ada programming language.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1988
Accession Number
ADA203049

Entities

People

  • James R. Stright

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Batch Processing
  • Computer Programming
  • Computer Programs
  • Computers
  • Difference Equations
  • Differential Equations
  • Electrical Engineering
  • Engineering
  • Equations
  • High Level Languages
  • Neural Networks
  • Nonlinear Dynamics
  • Programming Languages
  • Sine Waves
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Systems Analysis and Design
  • Wave Propagation and Nonlinear Chaotic Dynamics.

Technology Areas

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