Statistical estimation of multiple parameters via symbolic dynamic filtering

Chinmay Rao; Kushal Mukherjee; Soumik Sarkar; Asok Ray

doi:10.1016/j.sigpro.2008.11.018

Statistical estimation of multiple parameters via symbolic dynamic filtering

Chinmay Rao, Kushal Mukherjee, Soumik Sarkar, Asok Ray

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

This paper addresses statistical estimation of multiple parameters that may vary simultaneously but slowly relative to the process response in nonlinear dynamical systems. The estimation algorithm is sensor-data-driven and is built upon the concept of symbolic dynamic filtering for real-time execution on limited-memory platforms, such as local nodes in a sensor network. In this approach, the behavior patterns of the dynamical system are compactly generated as quasi-stationary probability vectors associated with the finite-state automata for symbolic dynamic representation. The estimation algorithm is validated on nonlinear electronic circuits that represent externally excited Duffing and unforced van der Pol systems. Confidence intervals are obtained for statistical estimation of two parameters in each of the systems.

Original language	English (US)
Pages (from-to)	981-988
Number of pages	8
Journal	Signal Processing
Volume	89
Issue number	6
DOIs	https://doi.org/10.1016/j.sigpro.2008.11.018
State	Published - Jun 2009

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Software
Signal Processing
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Access to Document

10.1016/j.sigpro.2008.11.018

Cite this

@article{8f993fb3c47545328534226c1a906392,

title = "Statistical estimation of multiple parameters via symbolic dynamic filtering",

abstract = "This paper addresses statistical estimation of multiple parameters that may vary simultaneously but slowly relative to the process response in nonlinear dynamical systems. The estimation algorithm is sensor-data-driven and is built upon the concept of symbolic dynamic filtering for real-time execution on limited-memory platforms, such as local nodes in a sensor network. In this approach, the behavior patterns of the dynamical system are compactly generated as quasi-stationary probability vectors associated with the finite-state automata for symbolic dynamic representation. The estimation algorithm is validated on nonlinear electronic circuits that represent externally excited Duffing and unforced van der Pol systems. Confidence intervals are obtained for statistical estimation of two parameters in each of the systems.",

author = "Chinmay Rao and Kushal Mukherjee and Soumik Sarkar and Asok Ray",

note = "Funding Information: This work has been supported in part by the US Army Research Office (ARO) under Grant no. W911NF-07-1-0376 and by NASA under Cooperative Agreement no. NNX07AK49A. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies. ",

year = "2009",

month = jun,

doi = "10.1016/j.sigpro.2008.11.018",

language = "English (US)",

volume = "89",

pages = "981--988",

journal = "Signal Processing",

issn = "0165-1684",

publisher = "Elsevier B.V.",

number = "6",

}

TY - JOUR

T1 - Statistical estimation of multiple parameters via symbolic dynamic filtering

AU - Rao, Chinmay

AU - Mukherjee, Kushal

AU - Sarkar, Soumik

AU - Ray, Asok

N1 - Funding Information: This work has been supported in part by the US Army Research Office (ARO) under Grant no. W911NF-07-1-0376 and by NASA under Cooperative Agreement no. NNX07AK49A. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies.

PY - 2009/6

Y1 - 2009/6

N2 - This paper addresses statistical estimation of multiple parameters that may vary simultaneously but slowly relative to the process response in nonlinear dynamical systems. The estimation algorithm is sensor-data-driven and is built upon the concept of symbolic dynamic filtering for real-time execution on limited-memory platforms, such as local nodes in a sensor network. In this approach, the behavior patterns of the dynamical system are compactly generated as quasi-stationary probability vectors associated with the finite-state automata for symbolic dynamic representation. The estimation algorithm is validated on nonlinear electronic circuits that represent externally excited Duffing and unforced van der Pol systems. Confidence intervals are obtained for statistical estimation of two parameters in each of the systems.

AB - This paper addresses statistical estimation of multiple parameters that may vary simultaneously but slowly relative to the process response in nonlinear dynamical systems. The estimation algorithm is sensor-data-driven and is built upon the concept of symbolic dynamic filtering for real-time execution on limited-memory platforms, such as local nodes in a sensor network. In this approach, the behavior patterns of the dynamical system are compactly generated as quasi-stationary probability vectors associated with the finite-state automata for symbolic dynamic representation. The estimation algorithm is validated on nonlinear electronic circuits that represent externally excited Duffing and unforced van der Pol systems. Confidence intervals are obtained for statistical estimation of two parameters in each of the systems.

UR - http://www.scopus.com/inward/record.url?scp=60749098242&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=60749098242&partnerID=8YFLogxK

U2 - 10.1016/j.sigpro.2008.11.018

DO - 10.1016/j.sigpro.2008.11.018

M3 - Article

AN - SCOPUS:60749098242

SN - 0165-1684

VL - 89

SP - 981

EP - 988

JO - Signal Processing

JF - Signal Processing

IS - 6

ER -

Statistical estimation of multiple parameters via symbolic dynamic filtering

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this