The Experts below are selected from a list of 12 Experts worldwide ranked by ideXlab platform
Belur, Madhu N. - One of the best experts on this subject based on the ideXlab platform.
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Interlacing properties of system-poles, system-Zeros and Spectral-Zeros in MIMO systems
2020Co-Authors: Kumar Sandeep, Belur, Madhu N.Abstract:SISO passive systems with just one type of memory/storage element (either only inductive or only capacitative) are known to have real poles and Zeros, and further, with the Zeros interlacing poles (ZIP). Due to a variety of definitions of the notion of a system Zero, and due to other reasons described in the paper, results involving ZIP have not been extended to MIMO systems. This paper formulates conditions under which MIMO systems too have interlaced poles and Zeros. This paper next focusses on the notion of a `Spectral Zero' of a system, which has been well-studied in various contexts: for example, Spectral factorization, optimal charging/discharging of a dissipative system, and even model order reduction. We formulate conditions under which the Spectral Zeros of a MIMO system are real, and further, conditions that guarantee that the system-Zeros, Spectral Zeros, and the poles are all interlaced. The techniques used in the proofs involve new results in Algebraic Riccati equations (ARE) and Hamiltonian matrices, and these results help in formulating new notions of positive-real balancing, and inter-relations with the existing notion of positive-real balancing; we also relate the positive-real singular values with the eigenvalues of the extremal ARE solutions in the proposed `quasi-balanced' forms
Kumar Sandeep - One of the best experts on this subject based on the ideXlab platform.
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Interlacing properties of system-poles, system-Zeros and Spectral-Zeros in MIMO systems
2020Co-Authors: Kumar Sandeep, Belur, Madhu N.Abstract:SISO passive systems with just one type of memory/storage element (either only inductive or only capacitative) are known to have real poles and Zeros, and further, with the Zeros interlacing poles (ZIP). Due to a variety of definitions of the notion of a system Zero, and due to other reasons described in the paper, results involving ZIP have not been extended to MIMO systems. This paper formulates conditions under which MIMO systems too have interlaced poles and Zeros. This paper next focusses on the notion of a `Spectral Zero' of a system, which has been well-studied in various contexts: for example, Spectral factorization, optimal charging/discharging of a dissipative system, and even model order reduction. We formulate conditions under which the Spectral Zeros of a MIMO system are real, and further, conditions that guarantee that the system-Zeros, Spectral Zeros, and the poles are all interlaced. The techniques used in the proofs involve new results in Algebraic Riccati equations (ARE) and Hamiltonian matrices, and these results help in formulating new notions of positive-real balancing, and inter-relations with the existing notion of positive-real balancing; we also relate the positive-real singular values with the eigenvalues of the extremal ARE solutions in the proposed `quasi-balanced' forms
Athanasios C Antoulas - One of the best experts on this subject based on the ideXlab platform.
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passivity preserving model reduction using dominant Spectral Zero interpolation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2008Co-Authors: Roxana R Ionutiu, Joost Rommes, Athanasios C AntoulasAbstract:In this paper, the dominant Spectral-Zero method (dominant SZM) is presented, a new passivity-preserving model-reduction method for circuit simulation. Passivity is guaranteed via Spectral-Zero interpolation, and a dominance criterion is proposed for selecting Spectral Zeros. Dominant SZM is implemented as an iterative eigenvalue-approximation problem using the subspace-accelerated dominant-pole algorithm. Passive circuits are reduced automatically irrespective of how the original system equations are formulated (e.g., circuit models containing controlled sources or susceptance elements). Dominant SZM gives comparable and often more accurate reduced models than known techniques such as PRIMA, modal approximation, or positive real balanced truncation.
Roxana R Ionutiu - One of the best experts on this subject based on the ideXlab platform.
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passivity preserving model reduction using dominant Spectral Zero interpolation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2008Co-Authors: Roxana R Ionutiu, Joost Rommes, Athanasios C AntoulasAbstract:In this paper, the dominant Spectral-Zero method (dominant SZM) is presented, a new passivity-preserving model-reduction method for circuit simulation. Passivity is guaranteed via Spectral-Zero interpolation, and a dominance criterion is proposed for selecting Spectral Zeros. Dominant SZM is implemented as an iterative eigenvalue-approximation problem using the subspace-accelerated dominant-pole algorithm. Passive circuits are reduced automatically irrespective of how the original system equations are formulated (e.g., circuit models containing controlled sources or susceptance elements). Dominant SZM gives comparable and often more accurate reduced models than known techniques such as PRIMA, modal approximation, or positive real balanced truncation.
Joost Rommes - One of the best experts on this subject based on the ideXlab platform.
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passivity preserving model reduction using dominant Spectral Zero interpolation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2008Co-Authors: Roxana R Ionutiu, Joost Rommes, Athanasios C AntoulasAbstract:In this paper, the dominant Spectral-Zero method (dominant SZM) is presented, a new passivity-preserving model-reduction method for circuit simulation. Passivity is guaranteed via Spectral-Zero interpolation, and a dominance criterion is proposed for selecting Spectral Zeros. Dominant SZM is implemented as an iterative eigenvalue-approximation problem using the subspace-accelerated dominant-pole algorithm. Passive circuits are reduced automatically irrespective of how the original system equations are formulated (e.g., circuit models containing controlled sources or susceptance elements). Dominant SZM gives comparable and often more accurate reduced models than known techniques such as PRIMA, modal approximation, or positive real balanced truncation.