The Experts below are selected from a list of 69 Experts worldwide ranked by ideXlab platform
Liping Cao - One of the best experts on this subject based on the ideXlab platform.
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fundamental study a concise functional neural network computing the largest modulus eigenvalues and their corresponding Eigenvectors of a real skew matrix
Theoretical Computer Science, 2006Co-Authors: Yiguang Liu, Zhisheng You, Liping CaoAbstract:Quick extraction of the largest modulus eigenvalues of a real antisymmetric matrix is important for some engineering applications. As neural network runs in concurrent and asynchronous manner in essence, using it to complete this calculation can achieve high speed. This paper introduces a concise functional neural network (FNN), which can be equivalently transformed into a Complex differential equation, to do this work. After obtaining the analytic solution of the equation, the convergence behaviors of this FNN are discussed. Simulation result indicates that with general initial Complex values, the network will converge to the Complex Eigenvector which corresponds to the eigenvalue whose imaginary part is positive, and modulus is the largest of all eigenvalues. Comparing with other neural networks designed for the like aim, this network is applicable to real skew matrices.
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Letter: A functional neural network for computing the largest modulus eigenvalues and their corresponding Eigenvectors of an anti-symmetric matrix
Neurocomputing, 2005Co-Authors: Yiguang Liu, Zhisheng You, Liping CaoAbstract:Efficient computation of the largest modulus eigenvalues of a real anti-symmetric matrix is a very important problem in engineering. Using a neural network to complete these operations is in an asynchronous manner and can achieve high performance. This paper proposes a functional neural network (FNN) that can be transformed into a Complex differential equation to do this work. Firstly, the mathematical analytic solution of the equation is received, and then the convergence properties of this FNN are analyzed. The simulation result indicates that with general initial Complex values, the network will converge to the Complex Eigenvector corresponding to the eigenvalue whose imaginary part is positive, and modulus is the largest of all eigenvalues. Comparing with other neural networks used for computing eigenvalues and Eigenvectors, this network is adaptive to real anti-symmetric matrices for completing these operations.
Yiguang Liu - One of the best experts on this subject based on the ideXlab platform.
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fundamental study a concise functional neural network computing the largest modulus eigenvalues and their corresponding Eigenvectors of a real skew matrix
Theoretical Computer Science, 2006Co-Authors: Yiguang Liu, Zhisheng You, Liping CaoAbstract:Quick extraction of the largest modulus eigenvalues of a real antisymmetric matrix is important for some engineering applications. As neural network runs in concurrent and asynchronous manner in essence, using it to complete this calculation can achieve high speed. This paper introduces a concise functional neural network (FNN), which can be equivalently transformed into a Complex differential equation, to do this work. After obtaining the analytic solution of the equation, the convergence behaviors of this FNN are discussed. Simulation result indicates that with general initial Complex values, the network will converge to the Complex Eigenvector which corresponds to the eigenvalue whose imaginary part is positive, and modulus is the largest of all eigenvalues. Comparing with other neural networks designed for the like aim, this network is applicable to real skew matrices.
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Letter: A functional neural network for computing the largest modulus eigenvalues and their corresponding Eigenvectors of an anti-symmetric matrix
Neurocomputing, 2005Co-Authors: Yiguang Liu, Zhisheng You, Liping CaoAbstract:Efficient computation of the largest modulus eigenvalues of a real anti-symmetric matrix is a very important problem in engineering. Using a neural network to complete these operations is in an asynchronous manner and can achieve high performance. This paper proposes a functional neural network (FNN) that can be transformed into a Complex differential equation to do this work. Firstly, the mathematical analytic solution of the equation is received, and then the convergence properties of this FNN are analyzed. The simulation result indicates that with general initial Complex values, the network will converge to the Complex Eigenvector corresponding to the eigenvalue whose imaginary part is positive, and modulus is the largest of all eigenvalues. Comparing with other neural networks used for computing eigenvalues and Eigenvectors, this network is adaptive to real anti-symmetric matrices for completing these operations.
Zhisheng You - One of the best experts on this subject based on the ideXlab platform.
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fundamental study a concise functional neural network computing the largest modulus eigenvalues and their corresponding Eigenvectors of a real skew matrix
Theoretical Computer Science, 2006Co-Authors: Yiguang Liu, Zhisheng You, Liping CaoAbstract:Quick extraction of the largest modulus eigenvalues of a real antisymmetric matrix is important for some engineering applications. As neural network runs in concurrent and asynchronous manner in essence, using it to complete this calculation can achieve high speed. This paper introduces a concise functional neural network (FNN), which can be equivalently transformed into a Complex differential equation, to do this work. After obtaining the analytic solution of the equation, the convergence behaviors of this FNN are discussed. Simulation result indicates that with general initial Complex values, the network will converge to the Complex Eigenvector which corresponds to the eigenvalue whose imaginary part is positive, and modulus is the largest of all eigenvalues. Comparing with other neural networks designed for the like aim, this network is applicable to real skew matrices.
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Letter: A functional neural network for computing the largest modulus eigenvalues and their corresponding Eigenvectors of an anti-symmetric matrix
Neurocomputing, 2005Co-Authors: Yiguang Liu, Zhisheng You, Liping CaoAbstract:Efficient computation of the largest modulus eigenvalues of a real anti-symmetric matrix is a very important problem in engineering. Using a neural network to complete these operations is in an asynchronous manner and can achieve high performance. This paper proposes a functional neural network (FNN) that can be transformed into a Complex differential equation to do this work. Firstly, the mathematical analytic solution of the equation is received, and then the convergence properties of this FNN are analyzed. The simulation result indicates that with general initial Complex values, the network will converge to the Complex Eigenvector corresponding to the eigenvalue whose imaginary part is positive, and modulus is the largest of all eigenvalues. Comparing with other neural networks used for computing eigenvalues and Eigenvectors, this network is adaptive to real anti-symmetric matrices for completing these operations.
Dionisio Bernal - One of the best experts on this subject based on the ideXlab platform.
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Complex Eigenvector scaling from mass perturbations
Mechanical Systems and Signal Processing, 2014Co-Authors: Dionisio BernalAbstract:Abstract This paper presents an approach to normalize experimentally extracted Complex Eigenvectors so that their outer product gives transfer function residues. The approach, an implementation of the mass perturbation strategy, is exact for arbitrary perturbation magnitudes and number of sensors when the modal space is complete and is robust against modal truncation. It is shown that improvements over a sensitivity solution are significant when the relation between the eigenvalue and the perturbation magnitude is strongly nonlinear.
Vladimir V. Mokeyev - One of the best experts on this subject based on the ideXlab platform.
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A generalized Complex Eigenvector method for dynamic analysis of heterogeneous viscoelastic structures
International Journal for Numerical Methods in Engineering, 2001Co-Authors: Vladimir V. MokeyevAbstract:A generalized Complex Eigenvector method which can be used to a linear dynamic analysis of viscoelastic structures is described. Here dynamic analysis is understood as transient analysis and frequency response analysis. The generalized Complex Eigenvector method is based on nite element discretization of structure, approximation of viscoelastic properties by di erential operators and mode superposition technique. Coe cients of di erential operator are de ned from the condition of best coincidence of Complex characteristic of viscoelastic material and Complex characteristic of di erential operator in preset frequency range. Advantage of this method is that it allows to take into account the real changes of the viscoelastic property in frequency range. Also, the generalized Complex Eigenvector method permit to describe a viscoelastic properties by two functions (Complex Young’s modulus, Complex Poisson’s ratio). The method is veri ed with the help of comparing with solutions obtained by Complex modulus method. An in uence of viscoelastic Poisson’s ratio on transient and frequency responses of structure is demonstrated. Copyright ? 2001 John Wiley & Sons, Ltd.