In this article an innovative method for training regressive MLP networks is
presented, which is not subject to local minima. The Error-Back-Propagation
algorithm, proposed by William-Hinton-Rummelhart, has had the merit of
favouring the development of machine learning techniques, which has permeated
every branch of research and technology since the mid-1980s. This extraordinary
success is largely due to the black-box approach, but this same factor was also
seen as a limitation, as soon more challenging problems were approached. One of
the most critical aspects of the training algorithms was that of local minima
of the loss function, typically the mean squared error of the output on the
training set. In fact, as the most popular training algorithms are driven by
the derivatives of the loss function, there is no possibility to evaluate if a
reached minimum is local or global. The algorithm presented in this paper
avoids the problem of local minima, as the training is based on the properties
of the distribution of the training set, or better on its image internal to the
neural network. The performance of the algorithm is shown for a well-known
benchmark.

这篇文章介绍了一种创新的方法来训练回归 MLP 网络，该方法不受局部极小值的影响，通过基于训练集分布特性或更好地说是神经网络内部的图像来避免局部极小值问题，并通过一个著名的基准测试展示了算法的性能。