Testing potentials of dynamic quadratic neural unit for prediction of lung motion during respiration for tracking radiation therapy

Ivo Bukovsky, Kei Ichiji, Noriyasu Homma, Makoto Yoshizawa, Ricardo Rodriguez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

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 time series generated by chaotic MacKey-Glass equation, by another highly nonlinear periodic time series, and on real lung motion measured during patients respiration. The real time recurrent learning (RTRL) rule is derived for dynamic QNU in a matrix form that is also efficient for implementation. It is shown that the standalone QNU gives promising results on a longer prediction times of the lung position compared to results in recent literature. In the end, we show even more precise results of two QNUs implemented as two local nonlinear predictive models and thus we present and discus a promising direction for high precision prediction of lung motion.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOIs
Publication statusPublished - 2010 Dec 1
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain
Duration: 2010 Jul 182010 Jul 23

Other

Other2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
CountrySpain
CityBarcelona
Period10/7/1810/7/23

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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