This article presents a new subspace-based technique for reducing the noise of signals in time-series. In the proposed approach, the signal is initially represented as a data matrix. Then using Singular Value Decomposition (SVD), noisy data matrix is divided into signal subspace and noise subspace. In this subspace division, each derivative of the singular values with respect to rank order is u...

Subtitle A new subspace algorithm consistently identiies stochastic state space models directly from given output data, using only semi-innnite block Hankel matrices. Abstract In this paper, we derive a new subspace algorithm to consistently identify stochastic state space models from given output data without forming the covariance matrix and using only semi-innnite block Hankel matrices. The ...

Vinay Prasad Multiscale systems offer unique challenges in modeling and control. From a modeling viewpoint, these systems are of very high dimension. Most of the systems have a stochastic component, resulting in noisy outputs. Additionally, their models are usually not in standard state space form, meaning that the application of advanced control strategies is not straightforward. The small num...

A new subspace algorithm consistently identifies stochastic state space models directly from given output data, using only semi-infinite block Hankel matrices. Ala~raet-In this paper, we derive a new subspace algorithm to consistently identify stochastic state space models from given output data without forming the covariance matrix and using only semi-infinite block Hankel matrices. The algori...

The starting point of this work is a framework allowing to model systems with dynamic process creation, equipped with a procedure to detect symmetric executions (i.e., which differ only by the identities of processes). This allows to reduce the state space, potentially to an exponentially smaller size, and, because process identifiers are never reused, this also allows to reduce to finite size ...

We present the basic notions on subspace identiication algorithms for linear systems. These methods estimate state sequences or extended observability matrices directly from the given data, through an orthogonal or oblique projection of the row spaces of certain block Hankel matrices into the row spaces of others. The extraction of the state space model is then achieved through the solution of ...

Let $H$ and $K$ be compact subgroups of locally compact group $G$. By considering the double coset space $Ksetminus G/H$, which equipped with an $N$-strongly quasi invariant measure $mu$, for $1leq pleq +infty$, we make a norm decreasing linear map from $L^p(G)$ onto $L^p(Ksetminus G/H,mu)$ and demonstrate that it may be identified with a quotient space of $L^p(G)$. In addition, we illustrate t...

 This paper discusses the feasibility of wireless Terahertz communications links deployed in a metropolitan area and lays down a black-box system identification framework to model the large scale fading of such channels. The movement of the receiver is modeled in state space by an autonomous dynamic linear system whereas the geometric relations involved in the attenuation and multi-path propag...

This paper aims to address a finite-horizon model predictive control (MPC) for non-linear drum-type boiler-turbine system using system-identification method. Considering that the strong state coupling of mechanism model, subspace identification method is first utilized obtain linear state-space and transformed into an input–output model. By taking inputs outputs as states, augmented non-minimal...