Testing Potentials of Dynamic Quadratic Neural Unit for Prediction of Lung Motion during Respiration for Tracking Radiation Therapy
Result description
This paper presents a study of the dynamic (recurrent) quadratic neural unit (QNU) -a class of higher order network or a class of polynomial neural network- as applied to the prediction of lung respiration dynamics. Human lung motion during respiration features nonlinear dynamics and displays quasiperiodical or even chaotic behavior. An attractive approximation capability of the recurrent QNU are demonstrated on a long term prediction of artificial and real time series.
Keywords
Dynamic Quadratic Neural UnitPredictionLung MotionTracking Radiation TherapyReal Time Recurrent Learning
The result's identifiers
Result code in IS VaVaI
Result on the web
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5596748
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Testing Potentials of Dynamic Quadratic Neural Unit for Prediction of Lung Motion during Respiration for Tracking Radiation Therapy
Original language description
This paper presents a study of the dynamic (recurrent) quadratic neural unit (QNU) -a class of higher order network or a class of polynomial neural network- as applied to the prediction of lung respiration dynamics. Human lung motion during respiration features nonlinear dynamics and displays quasiperiodical or even chaotic behavior. An attractive approximation capability of the recurrent QNU are demonstrated on a long term prediction of artificial and real time series.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BC - Theory and management systems
OECD FORD branch
—
Result continuities
Project
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2010 IEEE World Congress on Computational Inteligence/ International Joint Conference on Neural Networks 2010
ISBN
978-1-4244-6917-8
ISSN
1098-7576
e-ISSN
—
Number of pages
6
Pages from-to
3906-3911
Publisher name
IEEE
Place of publication
Piscataway
Event location
Barcelona
Event date
Jul 18, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000287421403123
Basic information
Result type
D - Article in proceedings
CEP
BC - Theory and management systems
Year of implementation
2010