Adaptive splitting technique for Gaussian mixture models to solve Kolmogorov Equation

Kumar Vishwajeet; Puneet Singla

doi:10.1109/ACC.2014.6859240

Adaptive splitting technique for Gaussian mixture models to solve Kolmogorov Equation

Kumar Vishwajeet, Puneet Singla

Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

The accuracy and the computational complexity of a Gaussian mixture model depends upon the number of components. In a stochastic dynamical system, the number of these components must change over time to account for the change in the uncertainty over time. A new splitting technique is provided based on the minimization of Fokker Planck Kolmogorov Equation. The effect of the splitting on the other components is also discussed in the work.

Original language	English (US)
Title of host publication	2014 American Control Conference, ACC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5186-5191
Number of pages	6
ISBN (Print)	9781479932726
DOIs	https://doi.org/10.1109/ACC.2014.6859240
State	Published - 2014
Event	2014 American Control Conference, ACC 2014 - Portland, OR, United States Duration: Jun 4 2014 → Jun 6 2014

Publication series

Name	Proceedings of the American Control Conference
ISSN (Print)	0743-1619

Other

Other	2014 American Control Conference, ACC 2014
Country/Territory	United States
City	Portland, OR
Period	6/4/14 → 6/6/14

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2014.6859240

Cite this

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title = "Adaptive splitting technique for Gaussian mixture models to solve Kolmogorov Equation",

abstract = "The accuracy and the computational complexity of a Gaussian mixture model depends upon the number of components. In a stochastic dynamical system, the number of these components must change over time to account for the change in the uncertainty over time. A new splitting technique is provided based on the minimization of Fokker Planck Kolmogorov Equation. The effect of the splitting on the other components is also discussed in the work.",

author = "Kumar Vishwajeet and Puneet Singla",

year = "2014",

doi = "10.1109/ACC.2014.6859240",

language = "English (US)",

isbn = "9781479932726",

series = "Proceedings of the American Control Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5186--5191",

booktitle = "2014 American Control Conference, ACC 2014",

address = "United States",

note = "2014 American Control Conference, ACC 2014 ; Conference date: 04-06-2014 Through 06-06-2014",

}

Vishwajeet, K & Singla, P 2014, Adaptive splitting technique for Gaussian mixture models to solve Kolmogorov Equation. in 2014 American Control Conference, ACC 2014., 6859240, Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers Inc., pp. 5186-5191, 2014 American Control Conference, ACC 2014, Portland, OR, United States, 6/4/14. https://doi.org/10.1109/ACC.2014.6859240

Adaptive splitting technique for Gaussian mixture models to solve Kolmogorov Equation. / Vishwajeet, Kumar; Singla, Puneet.
2014 American Control Conference, ACC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 5186-5191 6859240 (Proceedings of the American Control Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Adaptive splitting technique for Gaussian mixture models to solve Kolmogorov Equation

AU - Vishwajeet, Kumar

AU - Singla, Puneet

PY - 2014

Y1 - 2014

N2 - The accuracy and the computational complexity of a Gaussian mixture model depends upon the number of components. In a stochastic dynamical system, the number of these components must change over time to account for the change in the uncertainty over time. A new splitting technique is provided based on the minimization of Fokker Planck Kolmogorov Equation. The effect of the splitting on the other components is also discussed in the work.

AB - The accuracy and the computational complexity of a Gaussian mixture model depends upon the number of components. In a stochastic dynamical system, the number of these components must change over time to account for the change in the uncertainty over time. A new splitting technique is provided based on the minimization of Fokker Planck Kolmogorov Equation. The effect of the splitting on the other components is also discussed in the work.

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M3 - Conference contribution

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SN - 9781479932726

T3 - Proceedings of the American Control Conference

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BT - 2014 American Control Conference, ACC 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 American Control Conference, ACC 2014

Y2 - 4 June 2014 through 6 June 2014

ER -

Adaptive splitting technique for Gaussian mixture models to solve Kolmogorov Equation

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