The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
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Recurrent neural networks for solving systems of complex-valued Linear Equations
Electronics Letters, 1992Co-Authors: Jun WangAbstract:Recurrent neural networks are presented for solving systems of Linear Equations involving complex-valued coefficients. The recurrent neural networks are shown to be able to generate unknown solutions of complex-valued Linear Equations. The configurations of the recurrent neural networks are described. An example is also discussed.
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Electronic realisation of recurrent neural network for solving simultaneous Linear Equations
Electronics Letters, 1992Co-Authors: Jun WangAbstract:An electronic neural network for solving simultaneous Linear Equations is presented. The proposed electronic neural network is able to generate real-time solutions to large-scale problems. The operating characteristics of an op amp based neural network is demonstrated via an illustrative example.
Yongjie Zhang - One of the best experts on this subject based on the ideXlab platform.
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Preconditioned Bi-conjugate Gradient Method of large-scale complex Linear Equations
2008 World Automation Congress, 2008Co-Authors: Yongjie ZhangAbstract:Bi-conjugate gradient method (BCG) has potential problems on slow convergence or divergence when complex Linear Equations are large-scale or coefficient matrix of complex Linear Equations is ill. Fortunately, appropriate preconditioning techniques can speed convergence by reducing condition number of ill matrix. Based on a real incomplete Cholesky decomposition preconditioner, a preconditioned method for complex Linear Equations is built in this paper, and then a preconditioned bi-conjugate gradient method (PBCG) is obtained by combining the preconditioner and bi-conjugate gradient method. Numerical examples show that the preconditioned bi-conjugate gradient method is speedy, high-performance and applicable to solve system of large-scale complex Linear Equations.
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Symbol LU Method of Large Scale Sparse Linear Equations
2008 8th International Symposium on Antennas Propagation and EM Theory, 2008Co-Authors: Yongjie ZhangAbstract:Coefficient matrix of Linear Equations from finite element method (FEM) is sparse and symmetrical. For the sake of saving CPU operational time and reducing storing requirement to computer, we introduce fully sparse strategy that stores only nonzero elements of symmetrical part by chain pattern. In order to save computational time to accesses data during LU factorization, we develop a symbol LU factorization method. It can minimize fill-in elements and reduce computational quantity of LU factorization. By an address index system and minimum full-in elements algorithm, efficiency of LU factorization can be improved significantly. Numerical experiments show that combination of the symbol LU factorization method and fully sparse storage structure can improve the algorithmic efficiency for FEM solution of large scaled sparse Linear Equations.
I.w. Sandberg - One of the best experts on this subject based on the ideXlab platform.
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Iterative solution of Linear Equations
IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 1994Co-Authors: I.w. SandbergAbstract:The problem of iteratively solving Linear Equations of the form Ax = b, for a solution x, given b and an operator A, arises in several contexts in the circuits and systems area. The author presents a theorem for the iterative solution of such Linear Equations.
Yueguang Li - One of the best experts on this subject based on the ideXlab platform.
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Ill-conditioned Linear Equations and its algorithm
Information Sciences, 2015Co-Authors: Dazhou Wang, Yueguang LiAbstract:In this paper, according to the characteristics of ill-conditioned Linear Equations. An improved Wilkinson algorithm(IWA) for solving ill-conditioned Linear Equations is proposed. An amendment factor is introduced to reduce the condition number of the coefficient matrix of ill-conditioned Linear Equations. An automatic step size is adopted to estimate the local error and change the step size correspondingly. The numerical results demonstrate that this new iterative algorithm is superior to other methods such as the amended conjugate gradient. The new algorithm is more applicable for solving ill-conditioned Linear Equations.
Karol Pąk - One of the best experts on this subject based on the ideXlab platform.
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Solutions of Linear Equations
Formalized Mathematics, 2008Co-Authors: Karol PąkAbstract:Summary. In this paper I present the Kronecker-Capelli theorem which states that a system of Linear Equations has a solution if and only if the rank of its coecient matrix is equal to the rank of its augmented matrix.
