System identification is the art and science of building mathematical models of dynamic
systems from observed input–output data. It can be seen as the interface between the real …

Estimation of functions from sparse and noisy data is a central theme in machine learning. In
the last few years, many algorithms have been developed that exploit Tikhonov …

Nonlinear system identification is an extremely broad topic, since every system that is not
linear is nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld …

Inferring the structure and dynamics of network models is critical to understanding the
functionality and control of complex systems, such as metabolic and regulatory biological …

We consider a nonlinear state–space model with the state transition and observation
functions expressed as basis function expansions. The coefficients in the basis function …

We present a novel nonparametric approach for identification of nonlinear systems.
Exploiting the framework of Gaussian regression, the unknown nonlinear system is seen as …

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) …

Abstract System identification is the art and science of building mathematical models of
dynamic systems from observed input-output data. It can be seen as the interface between …

Approaches to identification of nonlinear systems Page 1 Approaches to Identification of
Nonlinear Systems* Lennart LJUNG Division of Automatic Control, Linköing University, SE-58183 …

Linear time-invariant approximations of nonlinear systems are used in many applications
and can be obtained in several ways. For example, using system identification and the …