广义动态神经网络在生物医学数据中的应用。

Application of generalized dynamic neural networks to biomedical data.

作者信息

Leistritz Lutz, Galicki Miroslaw, Kochs Eberhard, Zwick Ernst Bernhard, Fitzek Clemens, Reichenbach Jürgen R, Witte Herbert

机构信息

Institute of Medical Statistics, Computer Sciences, and Documentation, Friedrich Schiller University Jena, Jena 07740, Germany.

出版信息

IEEE Trans Biomed Eng. 2006 Nov;53(11):2289-99. doi: 10.1109/TBME.2006.881766.

DOI:10.1109/TBME.2006.881766

PMID:17073335

Abstract

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.

摘要

本文综述了具有时变权重的连续递归神经网络在医学模式识别任务中的应用。概括了一种基于庞特里亚金极大值原理的通用学习算法，并给出了提高这些网络泛化能力的可能性。通过取自麻醉学、骨科和放射学领域的三个不同的实际例子证明了这些方法的有效性。