Block-oriented nonlinear models are popular in nonlinear system identification because of
their advantages of being simple to understand and easy to use. Many different identification …

Selecting a “best” model among several competing candidate models poses an often
encountered problem in water resources modeling (and other disciplines which employ …

Linear system identification [1]-[4] is a basic step in modern control design approaches.
Starting from experimental data, a linear dynamic time-invariant model is identified to …

Industrial robots are electro-mechanical systems with double integrator behaviour,
necessitating operation and model identification under closed-loop control conditions. The …

The Wiener-Hammerstein (WH) system is a popular and easy to understand class of Volterra
nonlinear dynamical system. It consists of a static nonlinearity positioned between two …

A recent hydrological impacts study in British Columbia, Canada, used an ensemble of 23
climate change simulations to assess potential future changes in streamflow. These …

Electrosurgery uses electric current to heat, coagulate and ligate tissue. The current flow
induces a voltage due to the bio-impedance of the tissue. The flow of the current should be …

This paper proposes a novel algorithm for training recurrent neural network models of
nonlinear dynamical systems from an input/output training dataset. Arbitrary convex and …

In this paper we discuss how to identify a mathematical model for a (non) linear dynamic
system starting from experimental data. In the initial step, the frequency response function is …

Identification of nonlinear systems is presented using a neural network variant known as the
temporal convolutional network (TCN). The identification capabilities of TCNs and standard …