State-space modeling provides a powerful tool for system identification and prediction. In linear state-space models the data are usually assumed to be Gaussian and the models have certain structural constraints such that they are identifiable. In this paper we propose a non-Gaussian state-space model which does not have such constraints. We prove that this model is fully identifiable. We then ...

This paper presents a reinterpretation of Joint Factor Analysis as a signal approximation methodology―based on ridge regression―using an overcomplete dictionary learned from data. A non-probabilistic perspective of the three fundamental steps in the JFA paradigm based on point estimates is provided. That is, model training, hyperparameter estimation and scoring stages are equated to signal codi...

State-space modeling provides a powerful tool for system identification and prediction. In linear state-space models the data are usually assumed to be Gaussian and the models have certain structural constraints such that they are identifiable. In this paper we propose a non-Gaussian state-space model which does not have such constraints. We prove that this model is fully identifiable. We then ...

In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation, referred to as two-step knowledge-aided iterative estimation of signal parameters via rotational invariance techniques (ESPRIT) method (Two-Step KAIESPRIT), which achieves more accurate estimates than those of prior art. The proposed Two-Step KAI-ESPRIT improves the estimation of the covariance matrix...

In this paper‎, ‎we suggest a fifth order convergence three-step method for solving system of nonlinear equations‎. ‎Each iteration of the method requires two function evaluations‎, ‎two first Fr'{e}chet derivative evaluations and two matrix inversions‎. ‎Hence‎, ‎the efficiency index is $5^{1/({2n+4n^{2}+frac{4}{3}n^{3}})}$‎, ‎which is better than that of other three-step methods‎. ‎The advant...

Power generation using wind turbines is a highly researched control field. Many control designs have been proposed based on continuous-time models like PI-control, or state observers with state feedback but without special regard to robustness to model uncertainties. The aim of this thesis was to design a robust digital controller for a wind turbine. The design was based on a discrete-time mode...