This paper studies the identification of Infinite Impulse Response (IIR) systems with binary-valued measurements. The key to our approach is to recast the initial identification problem into an identification problem in the set-membership framework. This enables us to identify the system using an identification algorithm devoted to IIR systems in such a framework. Although the proposed algorithm is designed for noise free data, an analysis is provided in the presence of noise and illustrative examples are provided to demonstrate the effectiveness of the algorithm.

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.

The work presented in this paper focuses on the recursive identification of finite impulse response (FIR) systems with binary valued measurements. The main difficulty with such an identification is the low resolution of the available data on the output. It is first shown that this low resolution can be sufficient for the identification of the system. Then, the identification problem is formally posed as a bounded error identification problem, this allows the use of a simple recursive algorithm. Performance of the proposed algorithm are analyzed. Finally, simulation results are given to demonstrate the efficiency of the method.