Application of generalized dynamic neural networks to biomedical data
L Leistritz, M Galicki, E Kochs, EB Zwick… - IEEE transactions on …, 2006 - ieeexplore.ieee.org
L Leistritz, M Galicki, E Kochs, EB Zwick, C Fitzek, JR Reichenbach, H Witte
IEEE transactions on biomedical engineering, 2006•ieeexplore.ieee.orgThis paper reviews the application of continuous recurrent neural networks with time-varying
weights to pattern recognition tasks in medicine. A general learning algorithm based on
Pontryagin's maximum principle is recapitulated, and possibilities of improving the
generalization capabilities of these networks are given. The effectiveness of the methods is
demonstrated by three different real-world examples taken from the fields of anesthesiology,
orthopedics, and radiology.
weights to pattern recognition tasks in medicine. A general learning algorithm based on
Pontryagin's maximum principle is recapitulated, and possibilities of improving the
generalization capabilities of these networks are given. The effectiveness of the methods is
demonstrated by three different real-world examples taken from the fields of anesthesiology,
orthopedics, and radiology.
This paper reviews the application of continuous recurrent neural networks with time-varying weights to pattern recognition tasks in medicine. A general learning algorithm based on Pontryagin's maximum principle is recapitulated, and possibilities of improving the generalization capabilities of these networks are given. The effectiveness of the methods is demonstrated by three different real-world examples taken from the fields of anesthesiology, orthopedics, and radiology.
ieeexplore.ieee.org
Showing the best result for this search. See all results