Search Results for author:
Found 6 papers, 1 papers with code
Dealing with Collinearity in Large-Scale Linear System Identification Using Gaussian Regression
Many problems arising in control require the determination of a mathematical model of the application.
Dealing with collinearity in large-scale linear system identification using Bayesian regularization
We consider the identification of large-scale linear and stable dynamic systems whose outputs may be the result of many correlated inputs.
Identification of Low Rank Vector Processes
We study modeling and identification of stationary processes with a spectral density matrix of low rank.
Modeling of Low Rank Time Series
In this paper we study hidden dynamical relations between the components of a discrete-time stochastic vector process and investigate their properties with respect to stability and causality.
Modeling and Identification of Low Rank Vector Processes
We study modeling and identification of processes with a spectral density matrix of low rank.
A Comparative Measurement Study of Deep Learning as a Service Framework
Big data powered Deep Learning (DL) and its applications have blossomed in recent years, fueled by three technological trends: a large amount of digitized data openly accessible, a growing number of DL software frameworks in open source and commercial markets, and a selection of affordable parallel computing hardware devices.
