Search Method for a Singular Model Taking into AccountSingular Regions in a Search Space

提出了一种新的多层感知器(MLP)学习方法SSF，该方法充分利用奇异区域来稳定、成功地找到与隐含单元数目相称的优解。在我们的实验中，SSF使用人工和真实的数据集工作得很好。我们还提出了另一种利用特征向量下降的MLP学习方法，并证明了它可以在平坦的奇异区域内移动以找到最优解。此外，我们得到了非常有前景的初步结果，即我们的SSF框架可以应用于复值MLP的学习，以找到无界或周期解。

We proposed a new learning method called SSF for multilayer perceptron (MLP), which makes good use of singular regions to stably and successfully find excellent solutions commensurate with the number of hidden units. SSF worked well in our experiments using artificial and real data sets. We also proposed another learning method for MLP which utilizes eigen vector descent, and showed that it moved through flat singular regions to find excellent solutions. Moreover, we got very promising preliminary results that our SSF framework can be applied to learning of complex-valued MLP to find unbounded or periodic solutions.