@Article{NM-16-193,
author = { H. M. Shao, W. Wu and W. B. Liu},
title = {Convergence of BP Algorithm for Training MLP with Linear Output},
journal = {Numerical Mathematics, a Journal of Chinese Universities},
year = {2007},
volume = {16},
number = {3},
pages = {193--202},
abstract = {
The capability of multilayer perceptrons (MLPs) for approximating
continuous functions with arbitrary accuracy has been demonstrated
in the past decades. Back propagation $($BP$)$ algorithm is the most
popular learning algorithm for training of MLPs. In this paper, a
simple iteration formula is used to select the learning rate for
each cycle of training procedure, and a convergence result is
presented for the BP algorithm for training  MLP with a hidden layer
and a linear output unit. The monotonicity of the error function is
also guaranteed during the training iteration.},
issn = {},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/nm/8053.html}
}