The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Brian D O Anderson - One of the best experts on this subject based on the ideXlab platform.
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finite time distributed linear equation solver for solutions with minimum l_1 norm
IEEE Transactions on Automatic Control, 2020Co-Authors: Jingqiu Zhou, Xuan Wang, Brian D O AndersonAbstract:This paper proposes a continuous-time distributed algorithm for multiagent Networks to achieve a solution with the minimum $l_1$ -norm to underdetermined linear equations. The proposed algorithm comes from a combination of the Filippov set-valued map with the projection-consensus flow. Given the Underlying Network is undirected and fixed, it is shown that the proposed algorithm drives all agents’ individual states to converge in finite time to a common value, which is the minimum $l_1$ -norm solution.
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Finite-Time Distributed Linear Equation Solver for Minimum $l_1$ Norm Solutions.
arXiv: Systems and Control, 2017Co-Authors: Jingqiu Zhou, Xuan Wang, Brian D O AndersonAbstract:This paper proposes distributed algorithms for multi-agent Networks to achieve a solution in finite time to a linear equation $Ax=b$ where $A$ has full row rank, and with the minimum $l_1$-norm in the underdetermined case (where $A$ has more columns than rows). The Underlying Network is assumed to be undirected and fixed, and an analytical proof is provided for the proposed algorithm to drive all agents' individual states to converge to a common value, viz a solution of $Ax=b$, which is the minimum $l_1$-norm solution in the underdetermined case. Numerical simulations are also provided as validation of the proposed algorithms.
Jingqiu Zhou - One of the best experts on this subject based on the ideXlab platform.
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finite time distributed linear equation solver for solutions with minimum l_1 norm
IEEE Transactions on Automatic Control, 2020Co-Authors: Jingqiu Zhou, Xuan Wang, Brian D O AndersonAbstract:This paper proposes a continuous-time distributed algorithm for multiagent Networks to achieve a solution with the minimum $l_1$ -norm to underdetermined linear equations. The proposed algorithm comes from a combination of the Filippov set-valued map with the projection-consensus flow. Given the Underlying Network is undirected and fixed, it is shown that the proposed algorithm drives all agents’ individual states to converge in finite time to a common value, which is the minimum $l_1$ -norm solution.
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Finite-Time Distributed Linear Equation Solver for Minimum $l_1$ Norm Solutions.
arXiv: Systems and Control, 2017Co-Authors: Jingqiu Zhou, Xuan Wang, Brian D O AndersonAbstract:This paper proposes distributed algorithms for multi-agent Networks to achieve a solution in finite time to a linear equation $Ax=b$ where $A$ has full row rank, and with the minimum $l_1$-norm in the underdetermined case (where $A$ has more columns than rows). The Underlying Network is assumed to be undirected and fixed, and an analytical proof is provided for the proposed algorithm to drive all agents' individual states to converge to a common value, viz a solution of $Ax=b$, which is the minimum $l_1$-norm solution in the underdetermined case. Numerical simulations are also provided as validation of the proposed algorithms.
Aravind Rajeswaran - One of the best experts on this subject based on the ideXlab platform.
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identifying topology of low voltage distribution Networks based on smart meter data
IEEE Transactions on Smart Grid, 2018Co-Authors: Satya Jayadev Pappu, Nirav Bhatt, Ramkrishna Pasumarthy, Aravind RajeswaranAbstract:In a power distribution Network, the Network topology information is essential for an efficient operation. This Network connectivity information is often not available at the low voltage (LV) level due to uninformed changes that happen from time to time. In this paper, we propose a novel data-driven approach to identify the Underlying Network topology for LV distribution Networks including the load phase connectivity from time series of energy measurements. The proposed method involves the application of principal component analysis and its graph-theoretic interpretation to infer the steady state Network topology from smart meter energy measurements. The method is demonstrated through simulation on randomly generated Networks and also on IEEE recognized Roy Billinton distribution test system.
Xiaofan Wang - One of the best experts on this subject based on the ideXlab platform.
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adaptive cluster synchronisation of coupled harmonic oscillators with multiple leaders
Iet Control Theory and Applications, 2013Co-Authors: Michael Z Q Chen, Xiaofan Wang, Hongwei Wang, Najl V ValeyevAbstract:In this study, the authors investigate the cluster synchronisation of coupled harmonic oscillators with multiple leaders in an undirected fixed Network. Unlike many existing algorithms for cluster synchronisation of complex dynamical Networks or group consensus of multi-agent systems, which require global information of the Underlying Network such as eigenvalues of the coupling matrix or centralised control protocols, we propose a novel decentralised adaptive cluster synchronisation protocol for coupled harmonic oscillators. By using the decentralised adaptive cluster synchronisation protocol and without using any global information of the Underlying Network, all oscillators in the same group asymptotically synchronise with the corresponding leader even when only one oscillator in each group has access to the information of the corresponding leader. Numerical simulation results are presented to illustrate the theoretical results.
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Effects of Network structure and routing strategy on Network capacity.
Physical Review E, 2006Co-Authors: Zhenyi Chen, Xiaofan WangAbstract:The capacity of maximum end-to-end traffic flow the Network is able to handle without overloading is an important index for Network performance in real communication systems. In this paper, we estimate the variations of Network capacity under different routing strategies for three different topologies. Simulation results reveal that the capacity depends on the Underlying Network structure and the capacity increases as the Network becomes more homogeneous. It is also observed that the Network capacity is greatly enhanced when the new traffic awareness routing strategy is adopted in each Network structure.
Xuan Wang - One of the best experts on this subject based on the ideXlab platform.
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finite time distributed linear equation solver for solutions with minimum l_1 norm
IEEE Transactions on Automatic Control, 2020Co-Authors: Jingqiu Zhou, Xuan Wang, Brian D O AndersonAbstract:This paper proposes a continuous-time distributed algorithm for multiagent Networks to achieve a solution with the minimum $l_1$ -norm to underdetermined linear equations. The proposed algorithm comes from a combination of the Filippov set-valued map with the projection-consensus flow. Given the Underlying Network is undirected and fixed, it is shown that the proposed algorithm drives all agents’ individual states to converge in finite time to a common value, which is the minimum $l_1$ -norm solution.
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Distributed algorithm for achieving finite-time minimum l1 norm solutions of linear equation
'Institute of Electrical and Electronics Engineers (IEEE)', 2019Co-Authors: Zhou Jingqiu, Xuan Wang, Mou Shaoshuai, Anderson BrianAbstract:Abstract— This paper proposes a distributed algorithm for multi-agent Networks to achieve a minimum l1-norm solution to a linear equation Ax = b where A has full row rank. When the Underlying Network is undirected and fixed, it is proved that the proposed algorithm drive all agents’ individual states to converge in finite-time to the same minimum l1-norm solution. Numerical simulations are also provided as validation of the proposed algorithms
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Finite-Time Distributed Linear Equation Solver for Minimum $l_1$ Norm Solutions.
arXiv: Systems and Control, 2017Co-Authors: Jingqiu Zhou, Xuan Wang, Brian D O AndersonAbstract:This paper proposes distributed algorithms for multi-agent Networks to achieve a solution in finite time to a linear equation $Ax=b$ where $A$ has full row rank, and with the minimum $l_1$-norm in the underdetermined case (where $A$ has more columns than rows). The Underlying Network is assumed to be undirected and fixed, and an analytical proof is provided for the proposed algorithm to drive all agents' individual states to converge to a common value, viz a solution of $Ax=b$, which is the minimum $l_1$-norm solution in the underdetermined case. Numerical simulations are also provided as validation of the proposed algorithms.
