Several approaches can be found in the literature to perform the identification of block oriented models (BOMs). In this sense, an important improvement is to achieve robust identification to cope with the presence of ...

The task of system identification is far from being a new one. It was initially proposed in the mid of the 20th century, and had then been extensively developed for linear systems, due to the demands of that time concerning ...

The task of system identification is far from being a new one. It was initially proposed in the mid of the 20th century, and had then been extensively developed for linear systems, due to the demands of that time concerning ...

Block-oriented models have proved to be useful as simple nonlinear models for a vast number of applications. They are described as a cascade of linear dynamic and nonlinear static blocks. They have emerged as an appealing ...

The Wiener-Hammerstein models are a class of non-linear models set by a cascade connection of two linear dynamic blocks separated by a block that represents a static-type non-linearity. This article approaches the ...

The subspace methods for system identification are a powerful tool for modelingvmultivariable linear systems in state space. These methods do not require that all statesvare directly measured. Combining the methods of ...

In this work, multi-input multi-output (MIMO) nonlinear process identification is dealt with. In particular, two Volterra-type models are discussed in the context of system identification. These models are: Memory Polynomial ...

We study the performance of Hammerstein predistorters (PD) to model and compensate nonlinear effects produced by a high power amplifier with memory. A novel Hammerstein model is introduced that includes, as the basic static ...