Passivity Enforcement for Descriptor Systems Via Matrix Pencil Perturbation

Wang, Yuanzhe; Zhang, Zheng; Koh, Cheng-Kok; Shi, Guoyong; Pang, G.K.H.; Wong, Ngai

doi:10.1109/tcad.2011.2174638

Cited by 21 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The the experimental results show that the proposed regularization can improve the classification performances of all these four loss function based classifiers. In the future, we will study how to apply the proposed algorithm on large scale dataset based on some distributed big data platforms [62,63,64,65] and use it to signal and power integrity applications [66,67,68,69,70,71,72]. : Experiment results on HEp-2 cell image data set.…”

Section: Discussionmentioning

confidence: 99%

Maximum mutual information regularized classification

Wang

Zhao

et al. 2015

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

show abstract

Section: Discussionmentioning

confidence: 99%

Maximum mutual information regularized classification

Wang

Zhao

et al. 2015

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…Such frequencies are identified by the purely imaginary eigenvalues of the Hamiltonian matrices. Generalizations to descriptor systems require SHH pencils, [48][49][50] which are also well established. Extension to the parameterized case has also been proposed, and several techniques based on sampling have been introduced for the identification of locally active regions.…”

Section: Characterization Through Hamiltonian Matrices and Pencilsmentioning

confidence: 99%

On checking dissipativity of parameterized linear and time‐invariant circuits and systems

Zanco

Grivet-Talocia

2022

Circuit Theory & Apps

View full text Add to dashboard Cite

A novel dissipativity characterization is proposed for linear time-invariant circuits and systems whose dynamic behavior depends on one external parameter. The proposed formulation extends to the parameterized case standard Hamiltonian-based algebraic dissipativity characterizations and is structured as an underdetermined multiparameter eigenvalue problem. Resulting from a polynomial parameterization, the proposed characterization leads to an effective set of algorithms that are able to determine the regions of local dissipativity and local activity in the frequency-parameter plane, which in turn can be exploited by dissipativity enforcement algorithms to produce uniformly passive models. The reference application of proposed technique is behavioral modeling of complex circuits or systems, whose dynamic behavior can be compressed into a low-order system through dedicated model order-reduction processes. Various examples ranging from integrated components to microstrip filters and networks are used to illustrate the proposed characterization.

show abstract

“…In case of such violations, several model perturbation methods can be used to correct the model coefficients and recover model passivity [1], [26]. Most prominent methods are based on Hamiltonian eigenvalue perturbation [22], [23], singular value/eigenvalue perturbation [24], [25], Positive or Bounded Real Lemma [17] enforcement based on semidefinite programming [27], H ∞ norm optimization via non-smooth (convex) optimization [28] or localization methods [29].…”

Section: Introductionmentioning

confidence: 99%

A Perturbation Scheme for Passivity Verification and Enforcement of Parameterized Macromodels

Grivet-Talocia

2017

IEEE Trans. Compon., Packag. Manufact. Technol.

View full text Add to dashboard Cite

This paper presents an algorithm for checking and enforcing passivity of behavioral reduced-order macromodels of LTI systems, whose frequency-domain (scattering) responses depend on external parameters. Such models, which are typically extracted from sampled input-output responses obtained from numerical solution of first-principle physical models, usually expressed as Partial Differential Equations, prove extremely useful in design flows, since they allow optimization, what-if or sensitivity analyses, and design centering.Starting from an implicit parameterization of both poles and residues of the model, as resulting from well-known model identification schemes based on the Generalized Sanathanan-Koerner iteration, we construct a parameter-dependent Skew-Hamiltonian/Hamiltonian matrix pencil. The iterative extraction of purely imaginary eigenvalues ot fhe pencil, combined with an adaptive sampling scheme in the parameter space, is able to identify all regions in the frequency-parameter plane where local passivity violations occur. Then, a singular value perturbation scheme is setup to iteratively correct the model coefficients, until all local passivity violations are eliminated. The final result is a corrected model, which is uniformly passive throughout the parameter range. Several numerical examples denomstrate the effectiveness of the proposed approach.

show abstract

Passivity Enforcement for Descriptor Systems Via Matrix Pencil Perturbation

Cited by 21 publications

References 47 publications

Maximum mutual information regularized classification

Maximum mutual information regularized classification

On checking dissipativity of parameterized linear and time‐invariant circuits and systems

A Perturbation Scheme for Passivity Verification and Enforcement of Parameterized Macromodels

Contact Info

Product

Resources

About