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emgr—The Empirical Gramian Framework
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System Gramian matrices are a well-known encoding for properties of input-output systems such as controllability, observability or minimality. These so-called system Gramians were developed in linear system theory for applications such as model order reduction of control systems. Empirical Gramians are an extension to the system Gramians for parametric and nonlinear systems as well as a data-driven method of computation. The empirical Gramian framework - emgr - implements the empirical Gramians in a uniform and configurable manner, with applications such as Gramian-based (nonlinear) model reduction, decentralized control, sensitivity analysis, parameter identification and combined state and parameter reduction.
Title: emgr—The Empirical Gramian Framework
Description:
System Gramian matrices are a well-known encoding for properties of input-output systems such as controllability, observability or minimality.
These so-called system Gramians were developed in linear system theory for applications such as model order reduction of control systems.
Empirical Gramians are an extension to the system Gramians for parametric and nonlinear systems as well as a data-driven method of computation.
The empirical Gramian framework - emgr - implements the empirical Gramians in a uniform and configurable manner, with applications such as Gramian-based (nonlinear) model reduction, decentralized control, sensitivity analysis, parameter identification and combined state and parameter reduction.
Related Results
emgr
– EMpirical GRamian Framework Version 5.99
emgr
– EMpirical GRamian Framework Version 5.99
Version 5.99 of the empirical Gramian framework –
emgr
– completes a development cycle which focused on parametric model order reduction of gas network mode...
System identification based on the cross Gramian
System identification based on the cross Gramian
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Spectral Decompositions of Gramians of Continuous Stationary Systems Given by Equations of State in Canonical Forms
Spectral Decompositions of Gramians of Continuous Stationary Systems Given by Equations of State in Canonical Forms
The application of transformations of the state equations of continuous linear and bilinear systems to the canonical form of controllability allows one to simplify the computation ...
Spectral Decompositions of Gramians of Continuous Stationary Systems Given by Equations of State in Canonical Forms
Spectral Decompositions of Gramians of Continuous Stationary Systems Given by Equations of State in Canonical Forms
The application of transformations of the state equations of continuous linear and bilinear systems to the canonical form of controllability allows one to simplify the computation ...
The Versatile Cross Gramian for System Theoretic Model Reduction and More
The Versatile Cross Gramian for System Theoretic Model Reduction and More
For input-output systems, the cross gramian matrix encodes controllability and observability information into a single matrix, which are essential to system-theoretic applications....
Eigenvalues and Eigenvectors in Controllability Analysis
Eigenvalues and Eigenvectors in Controllability Analysis
This chapter demonstrates the effectiveness of spectral theory in analyzing controllability property of linear and non-linear systems. System design, which is at the core of contro...
Schizophrenia Diagnosis via FFT and Wavelet Convolutional Neural Networks utilizing EEG signals
Schizophrenia Diagnosis via FFT and Wavelet Convolutional Neural Networks utilizing EEG signals
Abstract
BackgroundSchizophrenia is a chronic mental illness in which a person's perception of reality is distorted. Early diagnosis can help to manage symptoms and increas...
Spectral Decomposition of Gramians of Continuous Linear Systems in the Form of Hadamard Products
Spectral Decomposition of Gramians of Continuous Linear Systems in the Form of Hadamard Products
New possibilities of Gramian computation by using canonical transformations into diagonal, controllable and observable canonical forms are shown. With the help of such a technique ...

