Nonlinear problems have drawn great interest and extensive attention from engineers,
physicists and mathematicians and many other scientists because most real systems are …

In this paper, the authors outline the general principles behind an approach to Bayesian
system identification and highlight the benefits of adopting a Bayesian framework when …

The Volterra model can represent a wide range of nonlinear dynamical systems. However,
its practical use in nonlinear system identification is limited due to the exponentially growing …

Master Techniques and Successfully Build Models Using a Single Resource Vital to all data-
driven or measurement-based process operations, system identification is an interface that …

Identification of nonlinear dynamic systems remains challenging nowadays. Although the
nonlinear autoregressive with exogenous input (NARX) model is flexible to describe …

Bayesian nonlinear system identification for one of the major classes of dynamic model, the
nonlinear autoregressive with exogenous input (NARX) model, has not been widely studied …

In the design of nonlinear complex systems, the output responses of a complex system
subject to demanding loading conditions need to be assessed before the system can be …

One of the most powerful and versatile system identification frameworks of the last three
decades is the NARMAX/NARX 1 approach, which is based on a nonlinear discrete-time …

Manufacturing is usually performed as a sequence of operations such as forming,
machining, inspection, and assembly. A new challenge in manufacturing is to move towards …

A novel iterative learning algorithm is proposed to improve the classic Orthogonal Forward
Regression (OFR) algorithm in an attempt to produce an optimal solution under a purely …