A Mutual-Information based Transfer Suitability Metric for Industrial Control.

In this paper, we address the design of data-based Artificial Neural Networks (ANN) controllers. More specifically, we consider a scalable design based on a Transfer Learning approach where an ANN controller trained at a given source scenario is transferred to other target domains. In order to properly assess the transfer suitability of the controller, the adoption of a Transfer Suitability Metric (TSM) is required. And here resides the main goal of this paper: to develop a TSM able to measure the amount of information captured by a neural network to estimate a desired output from input data. To do so, we resort to Mutual Information (MI) studies addressing the learning process in a neural network. As shown in the paper, we propose a MI-based metric able to assess the transfer suitability while reducing metric computation complexity.