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Attractor Density Clustering

  • T. L. CarrollEmail author
  • J. M. Byers
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 6)

Abstract

Stationary dynamical systems have invariant measures (or densities) that are characteristic of the particular dynamical system. We develop a method to characterize this density by partitioning the attractor into the smallest regions in phase space that contain information about the structure of the attractor. To accomplish this, we develop a statistic that tells us if we get more information about our data by dividing a set of data points into partitions rather than just lumping all the points together. We use this method to show that not only can we detect small changes in an attractor from a circuit experiment, but we can also distinguish between a large set of numerically generated chaotic attractors designed by Sprott

Keywords

Gaussian Mixture Model Chaotic Attractor Dirichlet Distribution Attractor Density Gaussian Mixture Model Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.US Naval Research LabWashington, DCUSA

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