Identification of nonlinear anelastic models

System identification of nonlinear state-space models

This paper is concerned with the parameter estimation of a general class of nonlinear dynamic systems in state-space form. More specifically, a Maximum Likelihood (ML) framework is employed and an Expectation Maximisation (EM) algorithm is derived to compute these ML estimates. The Expectation (E) step involves solving a nonlinear state estimation problem, where the smoothed estimates of the st...

Blind Identification of Hammerstein Nonlinear Distortion Models

Compensation of nonlinear distortions is an issue of importance for the restoration of degraded audio material. It however remains a very challenging task, especially in cases when only a single instance of the degraded audio signal is available. Compared to other sources of distortion such as additive noise (hiss) or signal gaps, even the fundamental limits achievable by the restoration are ye...

Identification of nonlinear errors-in-variables models

The paper is about a generalization of a classical eigenvalue-decomposition method originally developed for errors–in-variables linear system identification to handle an important class of nonlinear problems. A number of examples are presented to call the attention to the most critical part of the procedure turning the identification problem to a generalized eigenvalue-eigenvector calculation p...

Identification of nonlinear systems using Polynomial Nonlinear State Space models

Nonlinear System Identification Using Heterogeneous Multiple Models

Multiple models are recognised by their abilities to accurately describe nonlinear dynamic behaviours of a wide variety of nonlinear systems with a tractable model in control engineering problems. Multiple models are built by the interpolation of a set of submodels according to a particular aggregation mechanism, with the heterogeneous multiple model being of particular interest. This multiple ...