Nonlinear Independent Component Analysis Using State-space Models

Hysteresis Identification using Nonlinear State-Space Models

Most studies tackling hysteresis identification in the technical literature follow white-box approaches, i.e. they rely on the assumption that measured data obey a specific hysteretic model. Such an assumption may be a hard requirement to handle in real applications, since hysteresis is a highly individualistic nonlinear behaviour. The present paper adopts a black-box approach based on nonlinea...

Independent Component Analysis using Gaussian Mixture Models

This paper discusses a method for performing independent component analysis exploiting Gaussian mixture models (GMMs). Previously most techniques that combine these methods have used GMMs to model the source signals. This paper considers a parsimonious method for modelling the observed signals. The GMM is fitted to the observed data using a modified version of the expectation maximisation algor...

Identification of nonlinear systems using Polynomial Nonlinear State Space models

Nonlinear Independent Component Analysis By

Independent component analysis is often approached from an information theoretic perspective employing specific sample estimates for the mutual information between the separated outputs. These approximations involve the nonparametric estimation of signal entropies. The common approach involves the estimation of these quantities and adaptation based on these criteria. In contrast, in this paper,...

Nonlinear Independent Component Analysis Using Ensemble Learning: Experiments and Discussion

In this paper, we present experimental results on a non-linear independent component analysis approach based on Bayesian ensemble learning. The theory of the method is discussed in a companion paper. Simulations with artiicial and natural data demonstrate the feasibility and good performance of the proposed approach. We also discuss the relationships of the method to other existing methods.