Learning-on-learning approach for modeling

Neural networks based on back-propagation learning algorithms and gradient descent algorithms are the first and the easiest tools developed for machine learning. They are still widespread nowadays, so much so by exploiting a huge number of different coding languages, between which MatLab, Python or Java, we have the possibility of using these training tools. But as highlighted in the past, these traditional neural networks suffer from their slow convergence rate. Aim of this paper is to revisit an algorithm to improve the speed of the learning phase, by exploiting the power of parallel computing to train a suitable number of auxiliary neural networks which work concurrently with the principal network. The implementation of the proposed algorithm in MatLab is shown in order to make evident the main difference with the traditional learning algorithms. Several examples, related to modeling of technological datasets from industrial environment, confirm the suitability of the proposed procedure.