On bi-proper mapping discretization of a regular descriptor system

2012 ◽  
Author(s):  
S. Kawai ◽  
N. Hori
Keyword(s):  
Descriptor System ◽  
Proper Mapping

Iterative criterion of the descriptor system asymptotic steadiness

2020 ◽  
Keyword(s):  
Iterative Algorithm ◽  
Search Algorithm ◽  
Descriptor System ◽  
Matrix Sign Function ◽  
Sign Function ◽  
Generalized Matrix ◽  
Criterion Matrix

An iterative criterion for the asymptotic steadiness of a linear descriptor system is considered. The criterion is based on an iterative algorithm for computing a generalized matrix sign-function. As an example, the problem of analyzing the asymptotic steadiness of a large descriptor system is given. Keywords linear descriptor system; steadiness criterion; matrix sign-function; search algorithm

scholarly journals Fault estimation methods in descriptor system with partially decoupled disturbances.

2020 ◽  
Vol 53 (2) ◽  
pp. 4272-4278
Author(s):  
Manh-Hung Do ◽  
Damien Koenig ◽  
Didier Theilliol
Keyword(s):  
Descriptor System ◽  
Estimation Methods ◽  
Fault Estimation

scholarly journals Deep neural networks for quantum circuit mapping

2021 ◽  
Author(s):  
Giovanni Acampora ◽  
Roberto Schiattarella
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Machine Learning Techniques ◽  
Quantum Computers ◽  
Physical Constraints ◽  
Proper Mapping ◽  
Correct Execution

AbstractQuantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy.

scholarly journals Design of regular positive and stable descriptor systems via state-feedbacks for descriptor continuous-time linear systems with singular pencils

2014 ◽  
Vol 24 (3) ◽  
pp. 289-297
Author(s):  
Tadeusz Kaczorek
Keyword(s):  
Linear Systems ◽  
Continuous Time ◽  
Special Form ◽  
The State ◽  
New Method ◽  
Descriptor Systems ◽  
Descriptor System ◽  
Asymptotically Stable ◽  
Numerical Example ◽  
Time Linear

Abstract A new method is proposed of design of regular positive and asymptotically stable descriptor systems by the use of state-feedbacks for descriptor continuous-time linear systems with singular pencils. The method is based on the reduction of the descriptor system by elementary row and column operations to special form. A procedure for the design of the state-feedbacks gain matrix is presented and illustrated by a numerical example

Pole placement with LMI constraint of fuzzy descriptor system

2015 ◽  
Vol 352 (7) ◽  
pp. 2665-2678 ◽  
Author(s):  
Renquan Lu ◽  
Meng Wang ◽  
Jianjun Bai ◽  
Anke Xue ◽  
Hongbo Zou
Keyword(s):  
Pole Placement ◽  
Descriptor System
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