Linder R, Pöppl S J
Institute for Medical Informatics, Medical University of Lübeck, FRG Ratzeburger Allee 160, 23538 Lübeck, Germany.
Stud Health Technol Inform. 2000;77:1220-5.
For practical applications, artificial neural networks have to meet several requirements: Mainly they should learn quick, classify accurate and behave robust. Programs should be user-friendly and should not need the presence of an expert for fine tuning diverse learning parameters. The present paper demonstrates an approach using an oversized network topology, adaptive propagation (APROP), a modified error function, and averaging outputs of four networks described for the first time. As an example, signals from different semiconductor gas sensors of an electronic nose were classified. The electronic nose smelt different types of edible oil with extremely different a-priori-probabilities. The fully-specified neural network classifier fulfilled the above mentioned demands. The new approach will be helpful not only for classifying olfactory signals automatically but also in many other fields in medicine, e.g. in data mining from medical databases.
对于实际应用而言，人工神经网络必须满足几个要求：主要是它们应学习迅速、分类准确且表现稳健。程序应便于用户使用，并且不需要专家在场来微调各种学习参数。本文展示了一种方法，该方法使用超大网络拓扑、自适应传播（APROP）、一种修改后的误差函数以及首次描述的四个网络的平均输出。例如，对来自电子鼻不同半导体气体传感器的信号进行了分类。该电子鼻嗅闻具有极不同先验概率的不同类型食用油。完全指定的神经网络分类器满足了上述要求。这种新方法不仅将有助于自动分类嗅觉信号，而且在医学的许多其他领域也会有所帮助，例如在医学数据库的数据挖掘中。
