Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Search: onr:"swepub:oai:DiVA.org:kth-218100" > Learning Stochastic...

1 of 1
Previous record
Next record
To hitlist

Details
MARC

Abdalmoaty, Mohamed,1986-KTH,Reglerteknik,System Identification,KTH, Reglerteknik (author)

Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors

BookEnglish2017

Publisher, publication year, extent ...

Stockholm, Sweden :KTH Royal Institute of Technology,2017
186 s.
electronicrdacarrier

Numbers

LIBRIS-ID:oai:DiVA.org:kth-218100
ISBN:9789177296249
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218100URI
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-474170URI

Supplementary language notes

Language:English
Summary in:English

Part of subdatabase

SwePubSwePub

Classification

Subject category:vet swepub-contenttype
Subject category:lic swepub-publicationtype

Series

TRITA-EE,1653-5146 ;2017:172

Notes

QC 20171128
The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. Albeit asymptotically optimal, these methods come with several computational challenges and fundamental limitations.The contributions of this thesis can be divided into two main parts. In the first part, approximate solutions to the maximum likelihood problem are explored. Both analytical and numerical approaches, based on the expectation-maximization algorithm and the quasi-Newton algorithm, are considered. While analytic approximations are difficult to analyze, asymptotic guarantees can be established for methods based on Monte Carlo approximations. Yet, Monte Carlo methods come with their own computational difficulties; sampling in high-dimensional spaces requires an efficient proposal distribution to reduce the number of required samples to a reasonable value.In the second part, relatively simple prediction error method estimators are proposed. They are based on non-stationary one-step ahead predictors which are linear in the observed outputs, but are nonlinear in the (assumed known) input. These predictors rely only on the first two moments of the model and the computation of the likelihood function is not required. Consequently, the resulting estimators are defined via analytically tractable objective functions in several relevant cases. It is shown that, under mild assumptions, the estimators are consistent and asymptotically normal. In cases where the first two moments are analytically intractable due to the complexity of the model, it is possible to resort to vanilla Monte Carlo approximations. Several numerical examples demonstrate a good performance of the suggested estimators in several cases that are usually considered challenging.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Reglerteknik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Control Engineering hsv//eng
TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Signalbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing hsv//eng
TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Annan elektroteknik och elektronik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering hsv//eng
NATURVETENSKAP Matematik Sannolikhetsteori och statistik hsv//swe
NATURAL SCIENCES Mathematics Probability Theory and Statistics hsv//eng
Stochastic Nonlinear Systems
Nonlinear System Identification
Learning Dynamical Models
Maximum Likelihood
Estimation
Process Disturbance
Prediction Error Method
Non-stationary Linear Predictors
Intractable Likelihood
Latent Variable Models
Electrical Engineering
Elektro- och systemteknik

Added entries (persons, corporate bodies, meetings, titles ...)

Hjalmarsson, Håkan,ProfessorKTH,Reglerteknik,KTH, Reglerteknik(Swepub:kth)u10a8l40 (thesis advisor)
Olsson, Jimmy,Associate ProfessorKTH,Matematisk statistik,KTH, Matematisk statistik (opponent)
KTHReglerteknik (creator_code:org_t)

Internet link

Find in a library

Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Pred... (Search the publication in LIBRIS)

To the university's database

1 of 1
Previous record
Next record
To hitlist

Find more in SwePub

By the author/editor: Abdalmoaty, Moha ...; Hjalmarsson, Håk ...; Olsson, Jimmy, A ...

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Control Engineer ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Signal Processin ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Other Electrical ...

NATURAL SCIENCES: NATURAL SCIENCES; and Mathematics; and Probability Theo ...

Parts in the series: TRITA-EE,

By the university: Royal Institute of Technology; Uppsala University

Search outside SwePub

Extend your search to:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se