Identification of systems using binary sensors via Support Vector Machines

Abstract

In this paper, we consider the identification of systems based on binary measurements of the output. The linear part of the system is parameterized by a Finite Impulse Response filter and the binary sensor is parameterized by a threshold. The idea is to formulate the identification problem as a classification problem. This formulation allows the use of supervised learning algorithm such as Support Vector Machines (SVM). Simulation examples are given to illustrate the performance of the presented method.