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PREDICTING SYSTEM TRAJECTORIES TOWARD CRITICAL TRANSITIONS

  • US 20170308505A1
  • Filed: 03/13/2014
  • Published: 10/26/2017
  • Est. Priority Date: 03/14/2013
  • Status: Active Grant
First Claim
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1. A system for predicting system trajectories toward critical transitions, the system comprising:

  • one or more processors installed as an embedded decision support module in a vehicle entity and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform operations of;

    transforming a set of multivariate time series of observables of a complex system into a set of symbolic multivariate time series, wherein the complex system is a heterogeneously networked dynamical system of vehicle entities;

    determining a transfer entropy (TE) measure between two time series, wherein the TE measure quantifies an amount of information transfer from a source vehicle entity to a destination vehicle entity in the complex system;

    determining an associative transfer entropy (ATE) measure by decomposing the TE measure to associative states of asymmetric, directional information flows, the associative states being an ATE+ positive influence class and an ATE−

    negative influence class,wherein an influence from the source vehicle entity on the destination vehicle entity is positively correlated in the ATE+ positive influence class such that if a value of the source vehicle entity is increasing, a value of the destination vehicle entity is increasing, and an influence from the source vehicle entity on the destination vehicle entity is negatively correlated in the ATE−

    negative influence class such that if a value of the source vehicle entity is increasing, a value of the destination vehicle entity is decreasing;

    estimating ATE+, TE, and ATE−

    trajectories over time; and

    predicting a critical transition in the complex system using at least one of the ATE+, TE, and ATE−

    trajectories for analysis of directional information influences among vehicle entities of the complex system to avoid a catastrophic failure of the complex system.

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