Evaluation of neural network variable influence measures for process control

Decision-making frequently involves identifying how to change input parameters in a given process in order to effect a directed change in the process output. Artificial neural networks have been used extensively to model business and manufacturing processes and there are several existing neural network-based influence measures that allow a decision-maker to assess the relative impact of each variable on process performance. The purpose of this paper is to review those neural network-based measures of variable influence, and to identify the combination of those measures that results in a comprehensive approach to characterizing variable influence within a trained neural network model. We then demonstrate how this comprehensive approach can be used as a tool to guide decision makers in dynamic process control.