Many researchers have investigated the time-varying (TV) matrix pseudoinverse problem in recent years, for its importance addressing TV problems science and engineering. In this paper, of calculating inverse or an arbitrary real is considered addressed using singular value decomposition (SVD) zeroing neural network (ZNN) approaches. Since SVD frequently used to compute a matrix, research propos...

Simultaneous seismic data denoising and reconstruction is a currently popular research subject in modern reflection seismology. Traditional rank-reduction based 3D seismic data denoising and reconstruction algorithm will cause strong residual noise in the reconstructed data and thus affect the following processing and interpretation tasks. In this paper, we propose an improved rank-reduction me...

We consider least-squares problems where the coefficient matrices A, b are unknown but bounded. We minimize the worst-case residual error using (convex) second-order cone programming, yielding an algorithm with complexity similar to one singular value decomposition of A. The method can be interpreted as a Tikhonov regularization procedure, with the advantage that it provides an exact bound on t...

The meridian is a concept central to traditional Chinese medical techniques such as acupuncture. There is no physically verifiable anatomical or histological basis for the existence of meridians. In Chinese medicine, the meridians are channels along which the energy of the psychological system is considered to flow. It has been proven that the resistance along the meridian channels is lower com...

Many radial basis function (RBF) methods contain a free shape parameter that plays an important role for the accuracy of the method. In most papers the authors end up choosing this shape parameter by trial and error or some other ad hoc means. In this paper, we propose applying the genetic algorithm to determine a good shape parameter of radial basis functions for the solution of partial differ...

We consider recovery of low-rank matrices from noisy data by hard thresholding of singular values, in which empirical singular values below a prescribed threshold λ are set to 0. We study the asymptotic MSE (AMSE) in a framework where the matrix size is large compared to the rank of the matrix to be recovered, and the signal-to-noise ratio of the low-rank piece stays constant. The AMSE-optimal ...

The article considers a new iterative algorithm for solving total least squares problems. A version of the implicit method simple iterations based on singular value decomposition is proposed biased normal system algebraic equations. use makes it possible to replace an ill-conditioned problem with sequence problems smaller condition number. This significantly increase computational stability and...

Abstract Randomized methods can be competitive for the solution of problems with a large matrix low rank. They also have been applied successfully to large-scale linear discrete ill-posed by Tikhonov regularization (Xiang and Zou in Inverse Probl 29:085008, 2013). This entails computation an approximation partial singular value decomposition A that is numerical The present paper compares random...