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Parameter Estimation Based on Set-valued Signals: Theory and Application

Parameter Estimation Based on Set-valued Signals: Theory and Application This paper summarizes the parameter estimation of systems with set-valued signals, which can be classified to three catalogs: one-time completed algorithms, iterative methods and recursive algorithms. For one-time completed algorithms, empirical measure method is one of the earliest methods to estimate parameters by using set-valued signals, which has been applied to the adaptive tracking of periodic target signals. The iterative methods seek numerical solutions of the maximum likelihood estimation, which have been applied to both complex diseases diagnosis and radar target recognition. The recursive algorithms are constructed via stochastic approximation and stochastic gradient methods, which have been applied to adaptive tracking of non-periodic signals. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Mathematicae Applicatae Sinica Springer Journals

Parameter Estimation Based on Set-valued Signals: Theory and Application

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Publisher
Springer Journals
Copyright
Copyright © 2019 by The Editorial Office of AMAS & Springer-Verlag GmbH Germany
Subject
Mathematics; Applications of Mathematics; Math Applications in Computer Science; Theoretical, Mathematical and Computational Physics
ISSN
0168-9673
eISSN
1618-3932
DOI
10.1007/s10255-019-0822-x
Publisher site
See Article on Publisher Site

Abstract

This paper summarizes the parameter estimation of systems with set-valued signals, which can be classified to three catalogs: one-time completed algorithms, iterative methods and recursive algorithms. For one-time completed algorithms, empirical measure method is one of the earliest methods to estimate parameters by using set-valued signals, which has been applied to the adaptive tracking of periodic target signals. The iterative methods seek numerical solutions of the maximum likelihood estimation, which have been applied to both complex diseases diagnosis and radar target recognition. The recursive algorithms are constructed via stochastic approximation and stochastic gradient methods, which have been applied to adaptive tracking of non-periodic signals.

Journal

Acta Mathematicae Applicatae Sinica – Springer Journals

Published: May 15, 2019

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