System identiication of linear dynamical systems using so-called subspace methods consists of two main steps. First, a signal subspace estimate is found. This usually corresponds to estimating the range space of the extended observability matrix. Then the system parameters are estimated from the subspace estimate. The main result of this note is explicit excitation conditions on the input signa...

It seems fair to say that current state-of-the-art subspace identification methods provide reliable results only when applied to plants operating in open loop. However feedback is present in a variety of practical situations (even though often one cannot directly recognize physical controllers which “close the loop”) and there is a need of reliable identification methods and algorithms which co...

Abstract: Full-state observers for linear systems use available measurements for the estimation of the entire state of a system. Reduced-order observers instead deliver an estimate only in the unmeasured state subspace while the state values in the measured subspace are taken directly from the measurements. This paper presents a combination of both types of observers which directly uses only pa...

A geometric approach for systems represented by a singular 2D Fornasini-Marchesini model is developed by introducing suitable notions of invariant subspace and controlled invariant subspace of the state space. The first notion is shown to be usefull in characterizing the set of compatible boundary conditions and in studying the existence and uniqueness of solutions to the state space equation o...

In this paper, we introduce subspace-frequently hypercyclic operators. We show that these operators are subspace-hypercyclic and there are subspace-hypercyclic  operators that are not subspace-frequently hypercyclic. There is a criterion like to subspace-hypercyclicity criterion that implies subspace-frequent hypercyclicity and if an operator $T$ satisfies this criterion, then $Toplus T$ is sub...

In this paper, a multi-objective collaborative optimization (MOCO) strategy is proposed for making decisions on distillation column group. Firstly, based data preprocessing, the operating modes of tower group are determined by use fuzzy C-means clustering method. Secondly, concept variable, discrete state-space model main towers constructed subspace identification Then, MOCO designed ethylene p...

this study develops and analyzes preconditioned krylov subspace methods to solve linear systemsarising from discretization of the time-independent space-fractional models. first, we apply shifted grunwald formulas to obtain a stable finite difference approximation to fractional advection-diffusion equations. then, we employee two preconditioned iterative methods, namely, the preconditioned gene...

Clustering has been recognized as an important and valuable capability in the data mining field. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, t...

Clustering has been recognized as an important and valuable capability in the data mining field. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, t...

Clustering has been recognized as an important and valuable capability in the data mining field. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, t...