Classification of Linear Processes Type Using Convolutional Neural Networks

Abstract

There is an increasing demand to develop fast and reliable models to identify the class of control systems in developing online and plug-and-play controllers. In this paper, to perform automatic, reliable, and fast classification of linear processes, it is proposed to use Convolutional Neural Networks (CNNs). A process can be: unstable or stable, integrally or self-regulated, non-minimum phase or minimum phase, first-order or second-order, oscillatory damping, or over-damping. We consider six different classes of linear processes accordingly. The CNN is designed and trained to predict the class of linear processes by taking only their step responses. The results show clearly that the CNN has high generalization and accuracy in determining the behavior class of the process even in the presence of noise, delay, and high order dynamics.