Dynamic systems are considered whose outputs can be represented either by a deterministic series of the input variables or by a stochastic series of brownian motion processes. Approximate representations of such series are proposed. The representations are based on the Hankel matrix representation of the series. First, it is shown that any Hankel matrix, belonging to suitable classes, can be approximated in the deterministic and stochastic sense by finite rank Hankel matrices. Then, a method is proposed to design bilinear realizations of finite rank Hankel matrices, effective in the deterministic and stochastic sense. © 1991 Taylor & Francis Group, LLC
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
The problem of optimal approximate system identification is addressed with a newly defined measure o...
Dynamic systems are considered whose outputs can be represented either by a deterministic series of ...
The approximation of a Hankel matrix by finite rank Hankel matrices is considered. A constructive pr...
The Ho-Kalman algorithm creates a minimum realization of a system, when given a series of determinis...
The Ho-Kalman algorithm creates a minimum realization of a system, when given a series of determinis...
A new subspace algorithm consistently identifies stochastic state space models directly from given o...
The subject of modelling and application of stochastic processes is too vast to be exhausted in a si...
In this work a dynamic state-space model was constructed using a Hankel matrix formulation. A novel ...
AbstractAfter introducing the notion of “dynamical interpretation functor” to provide a general meth...
This paper presents suboptimal solutions to the problem of Approximate Partial Realization: given a ...
A response approximation method for stochastically excited, nonlinear, dynamic systems is presented....
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
The problem of optimal approximate system identification is addressed with a newly defined measure o...
Dynamic systems are considered whose outputs can be represented either by a deterministic series of ...
The approximation of a Hankel matrix by finite rank Hankel matrices is considered. A constructive pr...
The Ho-Kalman algorithm creates a minimum realization of a system, when given a series of determinis...
The Ho-Kalman algorithm creates a minimum realization of a system, when given a series of determinis...
A new subspace algorithm consistently identifies stochastic state space models directly from given o...
The subject of modelling and application of stochastic processes is too vast to be exhausted in a si...
In this work a dynamic state-space model was constructed using a Hankel matrix formulation. A novel ...
AbstractAfter introducing the notion of “dynamical interpretation functor” to provide a general meth...
This paper presents suboptimal solutions to the problem of Approximate Partial Realization: given a ...
A response approximation method for stochastically excited, nonlinear, dynamic systems is presented....
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
The problem of optimal approximate system identification is addressed with a newly defined measure o...