Unified nonlinear modeling approach for machine learning and artificial intelligence (attractor assisted AI)

Patent metadata

Field	Value
Publication number	US-12488266-B2
Application number	US-202016999605-A
Country	US
Kind code	B2
Filing date	Aug 21, 2020
Priority date	Oct 18, 2017
Publication date	Dec 2, 2025
Grant date	Dec 2, 2025

Abstract

A method for predicting future behavior for a dynamic system using an artificial intelligence system implemented within a computer hardware system. A predetermined amount of a time series group of data from the dynamic system defining previous behavior of the dynamic system are received at the artificial intelligence system. An attractor is constructed from the time series group of data that defines the previous behavior of the dynamic system using the artificial intelligence system. The attractor models the previous behavior of the dynamic system based on the predetermined amount of the time series group of data of the dynamic system. A prediction horizon for the predetermined amount of the time series group of data is determined with the artificial intelligence system using an attractor dimension of the constructed attractor and a Lyapunov exponent of the constructed attractor. The prediction horizon increases logarithmically as a length of the predetermined amount of the time series group of data from the dynamic system increases linearly. Prediction values of future behavior of the dynamic system are generated with the artificial intelligence system using the constructed attractor and the determined prediction horizon.

First claim

What is claimed is:

1 . A method for predicting future behavior for a dynamic system using an artificial intelligence system, comprising:
implementing the artificial intelligence system within a programmable processing system; configuring a chaotic oscillator implemented within the artificial intelligence system within the programmable processing system for providing a chaotic time series; receiving a predetermined amount of a time series group of data from the dynamic system defining previous behavior of the dynamic system at the artificial intelligence system implemented within the programmable processing system; constructing an attractor from the time series group of data defining the previous behavior of the dynamic system using the artificial intelligence system implemented within the programmable processing system, the attractor modeling the previous behavior of the dynamic system based on the predetermined amount of the time series group of data of the dynamic system; determining a prediction horizon for the predetermined amount of the time series group of data with the artificial intelligence system implemented within the programmable processing system using an attractor dimension of the constructed attractor and a Lyapunov exponent of the constructed attractor, wherein the prediction horizon increases logarithmically as a length of the predetermined amount of the time series group of data from the dynamic system increases linearly; implementing the constructed attractor, the determined prediction horizon and the chaotic time series provided by the chaotic oscillator within the artificial intelligence system implemented within the programmable processing system, the artificial intelligence system providing nonlinear modeling and forecasting for predictive capabilities of the artificial intelligence system; generating predicted values of future behavior of data generated by the dynamic system with the artificial intelligence system implementing the programmable processing system using the constructed attractor, the determined prediction horizon and the chaotic time series provided by the chaotic oscillator; and predicting a future behavior of the dynamic system responsive to the generated predicted values using the artificial intelligence system.

2 . The method of claim 1 , wherein the step of constructing further comprises:
creating a time delay value for the predetermined amount of the time series group of data using the artificial intelligence system implemented within the programmable processing system; and constructing the attractor responsive to the predetermined amount of the time series group of data and the time delay value using the artificial intelligence system implemented within the programmable processing system.

3 . The method of claim 2 , wherein the step of creating the time delay value further comprises:
determining mutual information having a first local minimum for the time series group of data; and determining the time delay value using the first local minimum of the mutual information.

4 . The method of claim 1 , wherein the step of constructing further comprises:
determining drivers of the dynamic system using the artificial intelligence system implemented within the programmable processing system; and constructing the attractor responsive to determined drivers using the artificial intelligence system implemented within the programmable processing system.

5 . The method of claim 1 , wherein the step of constructing further comprises:
extracting invariants from the dynamic system using the artificial intelligence system implemented within the programmable processing system implemented without reference to underlying physics of the dynamic system; and constructing the attractor responsive to the invariants using the artificial intelligence system implemented within the programmable processing system.

6 . The method of claim 1 , wherein the step of determining the prediction horizon further comprises:
determining the attractor dimension of the attractor using the artificial intelligence system implemented within the programmable processing system; determining the Lyapunov exponent of the attractor using the artificial intelligence system implemented within the programmable processing system; and calculating the prediction horizon responsive to the attractor dimension and the Lyapunov exponent, wherein the prediction horizon increases logarithmically as the length of the predetermined amount of the time series group of data from the dynamic system increases linearly using the artificial intelligence system implemented within the programmable processing system.

7 . The method of claim 1 , wherein the step of generating the predicted values of future behavior further comprises:
selecting a plurality of neighbors within the time series group of data; examining data sets of the attractor for the plurality of neighbors; and generating the predicted values of the future behavior responsive to values of the data sets of the attractor for the plurality of neighbors.

8 . The method of claim 1 , wherein the step of predicting further comprises:
looking through a data set of the attractor for a nearest neighbor to a current point for which the predicted value is being generated; and predicting an evolution of a state of the current point based on what the nearest neighbor did at later times.

9 . The method of claim 8 further including the step of computing a distance to each point in a multidimensional data set when finding neighbors in the multidimensional data set.

10 . A method for predicting future behavior for a dynamic system using an artificial intelligence system, comprising:
implementing the artificial intelligence system within a graphics processing unit; receiving a predetermined amount of a time series group of data from the dynamic system defining previous behavior of the dynamic system at the artificial intelligence system implemented within the graphics processing unit; constructing an attractor from the time series group of data defining the previous behavior of the dynamic system using the artificial intelligence system implemented within the graphics processing unit, the attractor modeling the previous behavior of the dynamic system based on the predetermined amount of the time series group of data of the dynamic system, wherein the attractor predicts future behaviors of the dynamic system more accurately than a statistical model; determining a prediction horizon for the predetermined amount of the time series group of data with the artificial intelligence system implemented within the graphics processing unit using an attractor dimension of the constructed attractor and a Lyapunov exponent of the constructed attractor, wherein the prediction horizon increases logarithmically as a length of the predetermined amount of the time series group of data from the dynamic system increases linearly; implementing the constructed attractor and the determined prediction horizon within the artificial intelligence system implemented within the graphics processing unit, the artificial intelligence system providing nonlinear modeling and forecasting for predictive capabilities of the artificial intelligence system; generating predicted values of future behavior of data generated by the dynamic system with the artificial intelligence system implemented within the graphics processing unit and implementing the constructed attractor and the determined prediction horizon; and predicting a future behavior of the dynamic system responsive to the generated predicted values using the artificial intelligence system.

11 . The method

Assignees

• Nxgen Partners Ip Llc

Inventors

• Ashrafi Solyman

Classifications

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N10/60

  Quantum algorithms, e.g. based on quantum optimisation, quantum Fourier or Hadamard transforms · CPC title

• G06F16/9024

  Graphs; Linked lists (G06F16/9027 takes precedence) · CPC title

• G06N20/00

  Machine learning · CPC title

• G06T1/20

  Processor architectures; Processor configuration, e.g. pipelining · CPC title