In this paper we introduce a new method of model reduction for nonlinear systems with inputs and outputs. The method requires only standard matrix computations, and when applied to linear systems results in the usual balanced truncation. For nonlinear systems, the method makes used of the Karhunen-Lo`eve decomposition of the state-space, and is an extension of the method of empirical eigenfunctions used in fluid dynamics. We show that the new method is equivalent to balanced-truncation in the linear case, and perform an example reduction for a nonlinear mechanical system
In this paper, we extend a method for reduced order model derivation for finite dimensional systems ...
gramians, model reduction, balanced truncation The paper presents a novel approach for a balanced tr...
For linear control systems minimal realization theory and the related model reduction methods play a...
In this paper we introduce a new method of model reduction for nonlinear systems with inputs and ou...
Abstract: In this paper we introduce a new method of model reduction for nonlinear systems with inpu...
In this paper, we introduce a new method of model reduction for nonlinear control systems. Our appro...
The analysis of system models forms an important tool in the design of high-tech systems. However, t...
In formulating mathematical models for dynamical systems, obtaining a high degree of qualitative cor...
We introduce a data-driven order reduction method for nonlinear control systems, drawing on recent p...
Large complex mathematical models are regularly used for simulation and prediction. However, in cont...
We propose a new computationally efficient modeling method that captures a given translation symmetr...
The paper presents a novel approach for a balanced truncation style of model reduction of a perturba...
A new approach to model order reduction of nonlinear control systems is aimed at developing persiste...
The problem of model order reduction plays a mayor role in engineering as the complexity and the dim...
In this paper, we present an empirical balanced truncation method for nonlinear systems whose input ...
In this paper, we extend a method for reduced order model derivation for finite dimensional systems ...
gramians, model reduction, balanced truncation The paper presents a novel approach for a balanced tr...
For linear control systems minimal realization theory and the related model reduction methods play a...
In this paper we introduce a new method of model reduction for nonlinear systems with inputs and ou...
Abstract: In this paper we introduce a new method of model reduction for nonlinear systems with inpu...
In this paper, we introduce a new method of model reduction for nonlinear control systems. Our appro...
The analysis of system models forms an important tool in the design of high-tech systems. However, t...
In formulating mathematical models for dynamical systems, obtaining a high degree of qualitative cor...
We introduce a data-driven order reduction method for nonlinear control systems, drawing on recent p...
Large complex mathematical models are regularly used for simulation and prediction. However, in cont...
We propose a new computationally efficient modeling method that captures a given translation symmetr...
The paper presents a novel approach for a balanced truncation style of model reduction of a perturba...
A new approach to model order reduction of nonlinear control systems is aimed at developing persiste...
The problem of model order reduction plays a mayor role in engineering as the complexity and the dim...
In this paper, we present an empirical balanced truncation method for nonlinear systems whose input ...
In this paper, we extend a method for reduced order model derivation for finite dimensional systems ...
gramians, model reduction, balanced truncation The paper presents a novel approach for a balanced tr...
For linear control systems minimal realization theory and the related model reduction methods play a...