This book presents an overview of the different errors-in-variables (EIV) methods that can be
used for system identification. Readers will explore the properties of an EIV problem. Such …

In this paper, we present a novel algorithm for estimating the parameters of a linear system
when the observed output signal is quantized. This question has relevance to many areas …

This work is concerned with identification of Wiener systems whose outputs are measured
by binary-valued sensors. The system consists of a linear FIR (finite impulse response) …

This paper considers the identification problem of the ARMA system followed by a binary
sensor, in which the internal variables are corrupted by additive ARMA noises. Recursive …

We present a new approach to parameter estimation problems based on binary
measurements, motivated by the need to add integrated low-cost self-test features to …

This paper investigates identification of Wiener systems with quantized inputs and binary-
valued output observations. By parameterizing the static nonlinear function and …

This paper investigates the recursive identification of the finite impulse response systems
whose outputs are subject to both the binary-valued quantization and the communication …

In this paper, we present an online identification method to the problem of parameter
estimation from binary observations. A recursive identification algorithm with low-storage …

In this paper, a novel recursive learning identification approach is proposed to estimate the
parameters of the Wiener systems with quantized output. By using a filter with adaptive …

Input design is of essential importance in system identification for providing sufficient
probing capabilities to guarantee convergence of parameter estimates to their true values …